Models that contain the Model Concept : Activity Patterns

(Spatial and/or temporal patterns of spiking activity.)
Re-display model names without descriptions
    Models   Description
1.  3D model of the olfactory bulb (Migliore et al. 2014)
This entry contains a link to a full HD version of movie 1 and the NEURON code of the paper: "Distributed organization of a brain microcircuit analysed by three-dimensional modeling: the olfactory bulb" by M Migliore, F Cavarretta, ML Hines, and GM Shepherd.
2.  3D olfactory bulb: operators (Migliore et al, 2015)
"... Using a 3D model of mitral and granule cell interactions supported by experimental findings, combined with a matrix-based representation of glomerular operations, we identify the mechanisms for forming one or more glomerular units in response to a given odor, how and to what extent the glomerular units interfere or interact with each other during learning, their computational role within the olfactory bulb microcircuit, and how their actions can be formalized into a theoretical framework in which the olfactory bulb can be considered to contain "odor operators" unique to each individual. ..."
3.  A bistable model of Spike-Wave seizure and background activity (Taylor et al. 2014)
This is a four-variable model (in the Amari formalism) of bistable Spike-Wave seizure dynamics and background activity (fixed point). The published code is the deterministic version of the model in the related publication. This model can be used to investigate seizure abatement using stimulation.
4.  A cardiac cell simulator (Puglisi and Bers 2001), applied to the QT interval (Busjahn et al 2004)
"LabHEART is an easy to use program that simulates the cardiac action potential, calcium transient and ionic currents. Key parameters such as ionic concentration, stimulus waveform and channel conductance can easily be changed by a click on an icon or dragging a slider. It is a powerfull tool for teaching and researching cardiac electrophysiology."
5.  A computational model of action selection in the basal ganglia (Suryanarayana et al 2019)
" ... Here, we incorporate newly revealed subgroups of neurons within the GPe into an existing computational model of the basal ganglia, and investigate their role in action selection. Three main results ensued. First, using previously used metrics for selection, the new extended connectivity improved the action selection performance of the model. Second, low frequency theta oscillations were observed in the subpopulation of the GPe (the TA or ‘arkypallidal’ neurons) which project exclusively to the striatum. These oscillations were suppressed by increased dopamine activity — revealing a possible link with symptoms of Parkinson’s disease. Third, a new phenomenon was observed in which the usual monotonic relationship between input to the basal ganglia and its output within an action ‘channel’ was, under some circumstances, reversed. ..."
6.  A cortical sheet mesoscopic model for investigating focal seizure onset dynamics (Wang et al. 2014)
The model uses realistically coupled, discretised, Wilson-Cowan units to describe the spatio-temporal activity of a cortical sheet. This model has been used the investigate the dynamic onset mechanisms of focal seizures.
7.  A cortico-cerebello-thalamo-cortical loop model under essential tremor (Zhang & Santaniello 2019)
We investigated the origins of oscillations under essential tremor (ET) by building a computational model of the cortico-cerebello-thalamo-cortical loop. It showed that an alteration of amplitudes and decay times of the GABAergic currents to the dentate nucleus can facilitate sustained oscillatory activity at tremor frequency throughout the network as well as a robust bursting activity in the thalamus, which is consistent with observations of thalamic tremor cells in ET patients. Tremor-related oscillations initiated in small neural populations and spread to a larger network as the synaptic dysfunction increased, while thalamic high-frequency stimulation suppressed tremor-related activity in thalamus but increased the oscillation frequency in the olivocerebellar loop.
8.  A detailed data-driven network model of prefrontal cortex (Hass et al 2016)
Data-based PFC-like circuit with layer 2/3 and 5, synaptic clustering, four types of interneurons and cell-type specific short-term synaptic plasticity; neuron parameters fitted to in vitro data, all other parameters constrained by experimental literature. Reproduces key features of in vivo resting state activity without specific tuning.
9.  A detailed Purkinje cell model (Masoli et al 2015)
The Purkinje cell is one of the most complex type of neuron in the central nervous system and is well known for its massive dendritic tree. The initiation of the action potential was theorized to be due to the high calcium channels presence in the dendritic tree but, in the last years, this idea was revised. In fact, the Axon Initial Segment, the first section of the axon was seen to be critical for the spontaneous generation of action potentials. The model reproduces the behaviours linked to the presence of this fundamental sections and the interplay with the other parts of the neuron.
10.  A dynamical model of the basal ganglia (Leblois et al 2006)
We propose a new model for the function and dysfunction of the basal ganglia (BG). The basal ganglia are a set of cerebral structures involved in motor control which dysfunction causes high-incidence pathologies such as Parkinson's disease (PD). Their precise motor functions remain unknown. The classical model of the BG that allowed for the discovery of new treatments for PD seems today outdated in several respects. Based on experimental observations, our model proposes a simple dynamical framework for the understanding of how BG may select motor programs to be executed. Moreover, we explain how this ability is lost and how tremor-related oscillations in neuronal activity may emerge in PD.
11.  A Fast Rhythmic Bursting Cell: in vivo cell modeling (Lee 2007)
One of the cellular mechanisms underlying the generation of gamma oscillations is a type of cortical pyramidal neuron named fast rhythmic bursting (FRB) cells. After cells from cats' primary visual cortices were filled with Neurobiotin, the brains were cut, and the cells were photographed. One FRB cell was chosen to be confocaled, reconstructed with Neurolucida software, and generated a detailed multi-compartmental model in the NEURON program. We explore firing properties of FRB cells and the role of enhanced Na+ conductance.
12.  A four compartmental model for ABPD complex in crustacean pyloric network (Maran et al. 2011)
"Central pattern generators (CPGs) frequently include bursting neurons that serve as pacemakers for rhythm generation. Phase resetting curves (PRCs) can provide insight into mechanisms underlying phase locking in such circuits. PRCs were constructed for a pacemaker bursting complex in the pyloric circuit in the stomatogastric ganglion of the lobster and crab. ..."
13.  A full-scale cortical microcircuit spiking network model (Shimoura et al 2018)
Reimplementation in BRIAN 2 simulator of a full-scale cortical microcircuit containing two cell types (excitatory and inhibitory) distributed in four layers, and represents the cortical network below a surface of 1 mm² (Potjans & Diesmann, 2014).
14.  A mathematical model of evoked calcium dynamics in astrocytes (Handy et al 2017)
" ...Here we present a qualitative analysis of a recent mathematical model of astrocyte calcium responses. We show how the major response types are generated in the model as a result of the underlying bifurcation structure. By varying key channel parameters, mimicking blockers used by experimentalists, we manipulate this underlying bifurcation structure and predict how the distributions of responses can change. We find that store-operated calcium channels, plasma membrane bound channels with little activity during calcium transients, have a surprisingly strong effect, underscoring the importance of considering these channels in both experiments and mathematical settings. ..."
15.  A microcircuit model of the frontal eye fields (Heinzle et al. 2007)
" ... we show that the canonical circuit (Douglas et al. 1989, Douglas and Martin 1991) can, with a few modifications, model the primate FEF. The spike-based network of integrate-and-fire neurons was tested in tasks that were used in electrophysiological experiments in behaving macaque monkeys. The dynamics of the model matched those of neurons observed in the FEF, and the behavioral results matched those observed in psychophysical experiments. The close relationship between the model and the cortical architecture allows a detailed comparison of the simulation results with physiological data and predicts details of the anatomical circuit of the FEF."
16.  A model for how correlation depends on the neuronal excitability type (Hong et al. 2012)
“ … Using simulations and experiments in rat hippocampal neurons, we show here that pairs of neurons receiving correlated input also exhibit correlations arising from precise spike-time synchronization. Contrary to rate comodulation, spike-time synchronization is unaffected by firing rate, thus enabling synchrony- and rate-based coding to operate independently. The type of output correlation depends on whether intrinsic neuron properties promote integration or coincidence detection: “ideal” integrators (with spike generation sensitive to stimulus mean) exhibit rate comodulation, whereas ideal coincidence detectors (with spike generation sensitive to stimulus variance) exhibit precise spike-time synchronization. … Our results explain how different types of correlations arise based on how individual neurons generate spikes, and why spike-time synchronization and rate comodulation can encode different stimulus properties. Our results also highlight the importance of neuronal properties for population-level coding insofar as neural networks can employ different coding schemes depending on the dominant operating mode of their constituent neurons. “
17.  A model for pituitary GH(3) lactotroph (Wu and Chang 2005)
The ATP-sensitive K(+) (K(ATP)) channels are composed of sulfonylurea receptor and inwardly rectifying K(+) channel (Kir6.2) subunit. These channels are regulated by intracellular ADP/ATP ratio and play a role in cellular metabolism. ... The objective of this study was to determine whether Diethyl pyrocarbonate (DEPC) modifies K(ATP)-channel activity in pituitary GH(3) cells. ... Simulation studies also demonstrated that the increased conductance of K(ATP)-channels used to mimic DEPC actions reduced the frequency of spontaneous action potentials and fluctuation of intracellular Ca(2+). The results indicate that chemical modification with DEPC enhances K(ATP)-channel activity and influences functional activities of pituitary GH(3) cells. See paper for more and details.
18.  A model of neuronal bursting using three coupled first order diff. eqs. (Hindmarsh & Rose 1984)
R Brette's Brian 2 implementation of the classic Hindmarsh-Rose 1984 dynamical system representing neuronal bursting.
19.  A model of the temporal pattern generator of C. elegans egg-laying behavior (Zhang et. al 2010)
"... We suggest that the HSN neuron is the executive neuron driving egg-laying events. We propose that the VC neurons act as "single egg counters" that inhibit HSN activity for short periods in response to individual egg-laying events. We further propose that the uv1 neuroendocrine cells are "cluster counters", which inhibit HSN activity for longer periods and are responsible for the time constant of the inactive phase. Together they form an integrated circuit that drives the clustered egg-laying pattern. ..."
20.  A Moth MGC Model-A HH network with quantitative rate reduction (Buckley & Nowotny 2011)
We provide the model used in Buckley & Nowotny (2011). It consists of a network of Hodgkin Huxley neurons coupled by slow GABA_B synapses which is run alongside a quantitative reduction described in the associated paper.
21.  A multi-compartment model for interneurons in the dLGN (Halnes et al. 2011)
This model for dLGN interneurons is presented in two parameterizations (P1 & P2), which were fitted to current-clamp data from two different interneurons (IN1 & IN2). The model qualitatively reproduces the responses in IN1 & IN2 under 8 different experimental condition, and quantitatively reproduces the I/O-relations (#spikes elicited as a function of injected current).
22.  A multilayer cortical model to study seizure propagation across microdomains (Basu et al. 2015)
A realistic neural network was used to simulate a region of neocortex to obtain extracellular LFPs from ‘virtual micro-electrodes’ and produce test data for comparison with multisite microelectrode recordings. A model was implemented in the GENESIS neurosimulator. A simulated region of cortex was represented by layers 2/3, 5/6 (interneurons and pyramidal cells) and layer 4 stelate cells, spaced at 25 µm in each horizontal direction. Pyramidal cells received AMPA and NMDA inputs from neighboring cells at the basal and apical dendrites. The LFP data was generated by simulating 16-site electrode array with the help of ‘efield’ objects arranged at the predetermined positions with respect to the surface of the simulated network. The LFP for the model is derived from a weighted average of the current sources summed over all cellular compartments. Cell models were taken from from Traub et al. (2005) J Neurophysiol 93(4):2194-232.
23.  A multiscale approach to analyze circadian rhythms (Vasalou & Henson, 2010) (CellML)
" ... We developed a firing rate code model to incorporate known electrophysiological properties of SCN (suprachiasmatic nucleus) pacemaker cells, including circadian dependent changes in membrane voltage and ion conductances. Calcium dynamics were included in the model as the putative link between electrical firing and gene expression. Individual ion currents exhibited oscillatory patterns matching experimental data both in current levels and phase relationships. VIP and GABA neurotransmitters, which encode synaptic signals across the SCN, were found to play critical roles in daily oscillations of membrane excitability and gene expression. Blocking various mechanisms of intracellular calcium accumulation by simulated pharmacological agents (nimodipine, IP3- and ryanodine-blockers) reproduced experimentally observed trends in firing rate dynamics and core-clock gene transcription. The intracellular calcium concentration was shown to regulate diverse circadian processes such as firing frequency, gene expression and system periodicity. The model predicted a direct relationship between firing frequency and gene expression amplitudes, demonstrated the importance of intracellular pathways for single cell behavior and provided a novel multiscale framework which captured characteristics of the SCN at both the electrophysiological and gene regulatory levels."
24.  A multiscale approach to analyze circadian rhythms (Vasalou & Henson, 2010) (SBML)
" ... We developed a firing rate code model to incorporate known electrophysiological properties of SCN (suprachiasmatic nucleus) pacemaker cells, including circadian dependent changes in membrane voltage and ion conductances. Calcium dynamics were included in the model as the putative link between electrical firing and gene expression. Individual ion currents exhibited oscillatory patterns matching experimental data both in current levels and phase relationships. VIP and GABA neurotransmitters, which encode synaptic signals across the SCN, were found to play critical roles in daily oscillations of membrane excitability and gene expression. Blocking various mechanisms of intracellular calcium accumulation by simulated pharmacological agents (nimodipine, IP3- and ryanodine-blockers) reproduced experimentally observed trends in firing rate dynamics and core-clock gene transcription. The intracellular calcium concentration was shown to regulate diverse circadian processes such as firing frequency, gene expression and system periodicity. The model predicted a direct relationship between firing frequency and gene expression amplitudes, demonstrated the importance of intracellular pathways for single cell behavior and provided a novel multiscale framework which captured characteristics of the SCN at both the electrophysiological and gene regulatory levels."
25.  A network model of tail withdrawal in Aplysia (White et al 1993)
The contributions of monosynaptic and polysynaptic circuitry to the tail-withdrawal reflex in the marine mollusk Aplysia californica were assessed by the use of physiologically based neural network models. Effects of monosynaptic circuitry were examined by the use of a two-layer network model with four sensory neurons in the input layer and one motor neuron in the output layer. Results of these simulations indicated that the monosynaptic circuit could not account fully for long-duration responses of tail motor neurons elicited by tail stimulation. A three-layer network model was constructed by interposing a layer of two excitatory interneurons between the input and output layers of the two-layer network model. The three-layer model could account for long-duration responses in motor neurons. Sensory neurons are a known site of plasticity in Aplysia. Synaptic plasticity at more than one locus modified dramatically the input-output relationship of the three-layer network model. This feature gave the model redundancy in its plastic properties and points to the possibility of distributed memory in the circuitry mediating withdrawal reflexes in Aplysia. Please see paper for more results and details.
26.  A network of AOB mitral cells that produces infra-slow bursting (Zylbertal et al. 2017)
Infra-slow rhythmic neuronal activity with very long (> 10 s) period duration was described in many brain areas but little is known about the role of this activity and the mechanisms that produce it. Here we combine experimental and computational methods to show that synchronous infra-slow bursting activity in mitral cells of the mouse accessory olfactory bulb (AOB) emerges from interplay between intracellular dynamics and network connectivity. In this novel mechanism, slow intracellular Na+ dynamics endow AOB mitral cells with a weak tendency to burst, which is further enhanced and stabilized by chemical and electrical synapses between them. Combined with the unique topology of the AOB network, infra-slow bursting enables integration and binding of multiple chemosensory stimuli over prolonged time scale. The example protocol simulates a two-glomeruli network with a single shared cell. Although each glomerulus is stimulated at a different time point, the activity of the entire population becomes synchronous (see paper Fig. 8)
27.  A phantom bursting mechanism for episodic bursting (Bertram et al 2008)
"We describe a novel dynamic mechanism for episodic or compound bursting oscillations, in which bursts of electrical impulses are clustered together into episodes, separated by long silent phases. We demonstrate the mechanism for episodic bursting using a minimal mathematical model for “phantom bursting.” Depending on the location in parameter space, this model can produce fast, medium, or slow bursting, or in the present case, fast, slow, and episodic bursting. The episodic bursting is modestly robust to noise and to parameter variation, and the effect that noise has on the episodic bursting pattern is quite different from that of an alternate episodic burst mechanism in which the slow envelope is produced by metabolic oscillations. This mechanism could account for episodic bursting produced in endocrine cells or neurons, such as pancreatic islets or gonadotropin releasing neurons of the hypothalamus."
28.  A simple integrative electrophysiological model of bursting GnRH neurons (Csercsik et al. 2011)
In this paper a modular model of the GnRH neuron is presented. For the aim of simplicity, the currents corresponding to fast time scales and action potential generation are described by an impulsive system, while the slower currents and calcium dynamics are described by usual ordinary differential equations (ODEs). The model is able to reproduce the depolarizing afterpotentials, afterhyperpolarization, periodic bursting behavior and the corresponding calcium transients observed in the case of GnRH neurons.
29.  A simulation method for the firing sequences of motor units (Jiang et al 2006)
" ... a novel model based on the Hodgkin–Huxley (HH) system is proposed, which has the ability to simulate the complex neurodynamics of the firing sequences of motor neurons. The model is presented at the cellular level and network level, and some simulation results from a simple 3-neuron network are presented to demonstrate its applications." See paper for more and details.
30.  A single column thalamocortical network model (Traub et al 2005)
To better understand population phenomena in thalamocortical neuronal ensembles, we have constructed a preliminary network model with 3,560 multicompartment neurons (containing soma, branching dendrites, and a portion of axon). Types of neurons included superficial pyramids (with regular spiking [RS] and fast rhythmic bursting [FRB] firing behaviors); RS spiny stellates; fast spiking (FS) interneurons, with basket-type and axoaxonic types of connectivity, and located in superficial and deep cortical layers; low threshold spiking (LTS) interneurons, that contacted principal cell dendrites; deep pyramids, that could have RS or intrinsic bursting (IB) firing behaviors, and endowed either with non-tufted apical dendrites or with long tufted apical dendrites; thalamocortical relay (TCR) cells; and nucleus reticularis (nRT) cells. To the extent possible, both electrophysiology and synaptic connectivity were based on published data, although many arbitrary choices were necessary.
31.  A spatial model of the intermediate superior colliculus (Moren et. al. 2013)
A spatial model of the intermediate superior colliculus. It reproduces the collicular saccade-generating output profile from NMDA receptor-driven burst neurons, shaped by integrative inhibitory feedback from spreading buildup neuron activity. The model is consistent with the view that collicular activity directly shapes the temporal profile of saccadic eye movements. We use the Adaptive exponential integrate and fire neuron model, augmented with an NMDA-like membrane potential-dependent receptor. In addition, we use a synthetic spike integrator model as a stand-in for a spike-integrator circuit in the reticular formation. NOTE: We use a couple of custom neuron models, so the supplied model file includes an entire version of NEST. I also include a patch that applies to a clean version of the simulator (see the doc/README).
32.  A spatially extended model for macroscopic spike-wave discharges (Taylor and Baier 2011)
A spatially extended neural field model for generating spike-wave based on the Amari (1977) model implemented in MATLAB.
33.  A spiking model of cortical broadcast and competition (Shanahan 2008)
"This paper presents a computer model of cortical broadcast and competition based on spiking neurons and inspired by the hypothesis of a global neuronal workspace underlying conscious information processing in the human brain. In the model, the hypothesised workspace is realised by a collection of recurrently interconnected regions capable of sustaining and disseminating a reverberating spatial pattern of activation. ..."
34.  A spiking neural network model of the Lateral Geniculate Nucleus (Sen-Bhattacharya et al 2017)
Using Izhikevich's spiking neuron models, to build a network with a biologically informed synaptic layout emulating the Lateral Geniculate Nucleus.
35.  A theory of ongoing activity in V1 (Goldberg et al 2004)
Ongoing spontaneous activity in the cerebral cortex exhibits complex spatiotemporal patterns in the absence of sensory stimuli. To elucidate the nature of this ongoing activity, we present a theoretical treatment of two contrasting scenarios of cortical dynamics: (1) fluctuations about a single background state and (2) wandering among multiple “attractor” states, which encode a single or several stimulus features. Studying simplified network rate models of the primary visual cortex (V1), we show that the single state scenario is characterized by fast and high-dimensional Gaussian-like fluctuations, whereas in the multiple state scenario the fluctuations are slow, low dimensional, and highly non-Gaussian. Studying a more realistic model that incorporates correlations in the feedforward input, spatially restricted cortical interactions, and an experimentally derived layout of pinwheels, we show that recent optical-imaging data of ongoing activity in V1 are consistent with the presence of either a single background state or multiple attractor states encoding many features.
36.  A two networks model of connectivity-dependent oscillatory activity (Avella OJ et al. 2014)
Activity in a cortical network may express a single oscillation frequency, alternate between two or more distinct frequencies, or continually express multiple frequencies. In addition, oscillation amplitude may fluctuate over time. Interactions between oscillatory networks may contribute, but their effects are poorly known. Here, we created a two model networks, one generating on its own a relatively slow frequency (slow network) and one generating a fast frequency (fast network). We chose the slow or the fast network as source network projecting feed-forward connections to the other, or target network, and systematically investigated how type and strength of inter-network connections affected target network activity. Our results strongly depended on three factors: the type of the relevant (main) connection, its strength and the amount of source synapses. For high inter-network connection strengths, we found that the source network could completely impose its rhythm on the target network. Interestingly, the slow network was more effective at imposing its rhythm on the fast network than the other way around. The strongest entrainment occurred when excitatory cells of the slow network projected to excitatory or inhibitory cells of the fast network. Just as observed in rat activity at the prefrontal cortex satisfies the behavior described above, such that together, our results suggest that input from other oscillating networks may markedly alter a network’s frequency spectrum and may partly be responsible for the rich repertoire of temporal oscillation patterns observed in the brain.
37.  A unified thalamic model of multiple distinct oscillations (Li, Henriquez and Fröhlich 2017)
We present a unified model of the thalamus that is capable of independently generating multiple distinct oscillations (delta, spindle, alpha and gamma oscillations) under different levels of acetylcholine (ACh) and norepinephrine (NE) modulation corresponding to different physiological conditions (deep sleep, light sleep, relaxed wakefulness and attention). The model also shows that entrainment of thalamic oscillations is state-dependent.
38.  ACh modulation in olfactory bulb and piriform cortex (de Almeida et al. 2013;Devore S, et al. 2014)
This matlab code was used in the papers de Almeida, Idiart and Linster, (2013), Devore S, de Almeida L, Linster C (2014) . This work uses a computational model of the OB and PC and their common cholinergic inputs to investigate how bulbar cholinergic modulation affects cortical odor processing.
39.  Actions of Rotenone on ionic currents and MEPPs in Mouse Hippocampal Neurons (Huang et al 2018)
" ... With the aid of patch-clamp technology and simulation modeling, the effects of (Rotenone) Rot on membrane ion currents present in mHippoE-14 cells were investigated. Results: Addition of Rot produced an inhibitory action on the peak amplitude of INa ...; however, neither activation nor inactivation kinetics of INa was changed during cell exposure to this compound. Addition of Rot produced little or no modifications in the steady-state inactivation curve of INa. Rot increased the amplitude of Ca2+-activated Cl- current in response to membrane depolarization ... . Moreover, when these cells were exposed to 10 µM Rot, a specific population of ATP-sensitive K+ channels ... was measured, despite its inability to alter single-channel conductance. Under current clamp condition, the frequency of miniature end-plate potentials in mHippoE-14 cells was significantly raised in the presence of Rot (10 µM) with no changes in their amplitude and time course of rise and decay. In simulated model of hippocampal neurons incorporated with chemical autaptic connection, increased autaptic strength to mimic the action of Rot was noted to change the bursting pattern with emergence of subthreshold potentials. Conclusions: The Rot effects presented herein might exert a significant action on functional activities of hippocampal neurons occurring in vivo. "
40.  Activity dependent changes in motoneurones (Dai Y et al 2002, Gardiner et al 2002)
These two papers review various experimental papers and examine the effects of activity on motoneurons in a similar 5 compartment model with 10 active conductances. Included are slow (S) and fast (F) type and fast fatigue resistant (FR) and fast fatigable (FF) models corresponding to the types of motoneurons. See papers for more and details.
41.  Activity dependent conductances in a neuron model (Liu et al. 1998)
"... We present a model of a stomatogastric ganglion (STG) neuron in which several Ca2+-dependent pathways are used to regulate the maximal conductances of membrane currents in an activity-dependent manner. Unlike previous models of this type, the regulation and modification of maximal conductances by electrical activity is unconstrained. The model has seven voltage-dependent membrane currents and uses three Ca2+ sensors acting on different time scales. ... The model suggests that neurons may regulate their conductances to maintain fixed patterns of electrical activity, rather than fixed maximal conductances, and that the regulation process requires feedback systems capable of reacting to changes of electrical activity on a number of different time scales."
42.  Activity dependent regulation of pacemaker channels by cAMP (Wang et al 2002)
Demonstration of the physiological consequences of the cyclic allosteric gating scheme for Ih mediated by HCN2 in thalamocortical relay cells.
43.  Activity patterns in a subthalamopallidal network of the basal ganglia model (Terman et al 2002)
"Based on recent experimental data, we have developed a conductance-based computational network model of the subthalamic nucleus and the external segment of the globus pallidus in the indirect pathway of the basal ganglia. Computer simulations and analysis of this model illuminate the roles of the coupling architecture of the network, and associated synaptic conductances, in modulating the activity patterns displayed by this network. Depending on the relationships of these coupling parameters, the network can support three general classes of sustained firing patterns: clustering, propagating waves, and repetitive spiking that may show little regularity or correlation. ...". Terman's XPP code and a partial implementation by Taylor Malone in NEURON and python are included.
44.  Afferent Integration in the NAcb MSP Cell (Wolf et al. 2005)
"We describe a computational model of the principal cell in the nucleus accumbens (NAcb), the medium spiny projection (MSP) neuron. The model neuron, constructed in NEURON, includes all of the known ionic currents in these cells and receives synaptic input from simulated spike trains via NMDA, AMPA, and GABAA receptors. ... results suggest that afferent information integration by the NAcb MSP cell may be compromised by pathology in which the NMDA current is altered or modulated, as has been proposed in both schizophrenia and addiction."
45.  Alcohol action in a detailed Purkinje neuron model and an efficient simplified model (Forrest 2015)
" ... we employ a novel reduction algorithm to produce a 2 compartment model of the cerebellar Purkinje neuron from a previously published, 1089 compartment model. It runs more than 400 times faster and retains the electrical behavior of the full model. So, it is more suitable for inclusion in large network models, where computational power is a limiting issue. We show the utility of this reduced model by demonstrating that it can replicate the full model’s response to alcohol, which can in turn reproduce experimental recordings from Purkinje neurons following alcohol application. ..."
46.  Ambiguous Encoding and Distorted Perception (Carlson and Kawasaki 2006)
"... In the weakly electric fish Eigenmannia, P- and T-type primary afferent fibers are specialized for encoding the amplitude and phase, respectively, of electrosensory stimuli. We used a stimulus estimation technique to quantify the ability of P- and T-units to encode random modulations in amplitude and phase. As expected, P-units exhibited a clear preference for encoding amplitude modulations, whereas T-units exhibited a clear preference for encoding phase modulations. Surprisingly, both types of afferents also encoded their nonpreferred stimulus attribute when it was presented in isolation or when the preferred stimulus attribute was sufficiently weak. Because afferent activity can be affected by modulations in either amplitude or phase, it is not possible to unambiguously distinguish between these two stimulus attributes by observing the activity of a single afferent fiber. Simple model neurons with a preference for encoding either amplitude or phase also encoded their nonpreferred stimulus attribute when it was presented in isolation, suggesting that such ambiguity is unavoidable. ... " See paper for more and details.
47.  An attractor network model of grid cells and theta-nested gamma oscillations (Pastoll et al., 2013)
A two population spiking continuous attractor model of grid cells. This model combines the attractor dynamics with theta-nested gamma oscillatory activity. It reproduces the behavioural response of grid cells (grid fields) in medial entorhinal cortex, while at the same time allowing for nested gamma oscillations of post-synaptic currents.
48.  An oscillatory neural model of multiple object tracking (Kazanovich and Borisyuk 2006)
An oscillatory neural network model of multiple object tracking is described. The model works with a set of identical visual objects moving around the screen. At the initial stage, the model selects into the focus of attention a subset of objects initially marked as targets. Other objects are used as distractors. The model aims to preserve the initial separation between targets and distractors while objects are moving. This is achieved by a proper interplay of synchronizing and desynchronizing interactions in a multilayer network, where each layer is responsible for tracking a single target. The results of the model simulation are presented and compared with experimental data. In agreement with experimental evidence, simulations with a larger number of targets have shown higher error rates. Also, the functioning of the model in the case of temporarily overlapping objects is presented.
49.  AOB mitral cell: persistent activity without feedback (Zylbertal et al., 2015)
Persistent activity has been reported in many brain areas and is hypothesized to mediate working memory and emotional brain states and to rely upon network or biophysical feedback. Here we demonstrate a novel mechanism by which persistent neuronal activity can be generated without feedback, relying instead on the slow removal of Na+ from neurons following bursts of activity. This is a realistic conductance-based model that was constructed using the detailed morphology of a single typical accessory olfactory bulb (AOB) mitral cell for which the electrophysiological properties were characterized.
50.  Artificial neuron model (Izhikevich 2003, 2004, 2007)
A set of models is presented based on 2 related parameterizations to reproduce spiking and bursting behavior of multiple types of cortical neurons and thalamic neurons. These models combine the biologically plausibility of Hodgkin Huxley-type dynamics and the computational efficiency of integrate-and-fire neurons. Using these model, one can simulate tens of thousands of spiking cortical neurons in real time (1 ms resolution) using a desktop PC.
51.  Asynchronous irregular and up/down states in excitatory and inhibitory NNs (Destexhe 2009)
"Randomly-connected networks of integrate-and-fire (IF) neurons are known to display asynchronous irregular (AI) activity states, which resemble the discharge activity recorded in the cerebral cortex of awake animals. ... Here, we investigate the occurrence of AI states in networks of nonlinear IF neurons, such as the adaptive exponential IF (Brette-Gerstner-Izhikevich) model. This model can display intrinsic properties such as low-threshold spike (LTS), regular spiking (RS) or fast-spiking (FS). We successively investigate the oscillatory and AI dynamics of thalamic, cortical and thalamocortical networks using such models. ..."
52.  Auditory cortex layer IV network model (Beeman 2013)
"... The primary objective of this modeling study was to determine the effects of axonal conduction velocity (often neglected, but significant), as well as synaptic time constants, on the ability of such a network to create and propagate cortical waves. ... The model is also being used to study the interaction between single and two-tone input and normal background activity, and the effects of synaptic depression from thalamic inputs. The simulation scripts have the additional purpose of serving as tutorial examples for the construction of cortical networks with GENESIS. The present model has fostered the development of the G-3 Python network analysis and visualization tools used in this study... It is my hope that this short tutorial and the example simulation scripts can provide a head start for a graduate student or postdoc who is beginning a cortical modeling project. "
53.  Auditory nerve model for predicting performance limits (Heinz et al 2001)
A computational auditory nerve (AN) model was developed for use in modeling psychophysical experiments with normal and impaired human listeners. In this phenomenological model, many physiologically vulnerable response properties associated with the cochlear amplifier are represented by a single nonlinear control mechanism, see paper for details. Several model versions are described that can be used to evaluate the relative effects of these nonlinear properties.
54.  Auditory nerve model with linear tuning (Heinz et al 2001)
A method for calculating psychophysical performance limits based on stochastic neural responses is introduced and compared to previous analytical methods for evaluating auditory discrimination of tone frequency and level. The method uses signal detection theory and a computational model for a population of auditory nerve (AN) fiber responses. Please see paper for details.
55.  Auditory nerve response model (Tan, Carney 2003)
A computational model was developed to simulate the responses of auditory-nerve (AN) fibers in cat. The incorporation of both the level-independent frequency glide and the level-dependent compressive nonlinearity into a phenomenological model for the AN was the primary focus of this work. The ability of this model to process arbitrary sound inputs makes it a useful tool for studying peripheral auditory processing.
56.  Auditory nerve response model (Zhang et al 2001)
A phenomenological model was developed to describe responses of high-spontaneous-rate auditory-nerve (AN) fibers, including several nonlinear response properties. The implementation of this model represents a relatively simple phenomenological description of a single mechanism that underlies several important nonlinear response properties of AN fibers. The model provides a tool for studying the roles of these nonlinearities in the encoding of simple and complex sounds in the responses of populations of AN fibers.
57.  Auditory nerve spontaneous rate histograms (Jackson and Carney 2005)
Histograms of spontaneous rate estimates of auditory nerve are well reproduced by models with two or three spontaneous rates and long range dependence.
58.  Axonal gap junctions produce fast oscillations in cerebellar Purkinje cells (Traub et al. 2008)
Examines how electrical coupling between proximal axons produces fast oscillations in cerebellar Purkinje cells. Traub RD, Middleton SJ, Knopfel T, Whittington MA (2008) Model of very fast (>75 Hz) network oscillations generated by electrical coupling between the proximal axons of cerebellar Purkinje cells. European Journal of Neuroscience.
59.  Axonal NaV1.6 Sodium Channels in AP Initiation of CA1 Pyramidal Neurons (Royeck et al. 2008)
"... We show that the Na+ channel NaV1.6 displays a striking aggregation at the AIS of cortical neurons. ... In combination with simulations using a realistic computer model of a CA1 pyramidal cell, our results imply that a hyperpolarized voltage-dependence of activation of AIS NaV1.6 channels is important both in determining spike threshold and localizing spike initiation to the AIS. ... These results suggest that NaV1.6 subunits at the AIS contribute significantly to its role as spike trigger zone and shape repetitive discharge properties of CA1 neurons."
60.  Basal ganglia network model of subthalamic deep brain stimulation (Hahn and McIntyre 2010)
Basal ganglia network model of parkinsonian activity and subthalamic deep brain stimulation in non-human primates from the article Instructions are provided in the README.txt file. Contact hahnp@ccf.org if you have any questions about the implementation of the model. Please include "ModelDB - BGnet" in the subject heading.
61.  Basal ganglia-thalamic network model for deep brain stimulation (So et al. 2011)
This is a model of the basal ganglia-thalamic network, modified from the Rubin and Terman model (High frequency stimulation of the Subthalamic Nucleus, Rubin and Terman 2004). We subsequently used this model to investigate the effectiveness of STN and GPi DBS as well as lesion when various proportions of local cells and fibers of passage were activated or silenced. The BG network exhibited characteristics consistent with published experimental data, both on the level of single cells and on the network level. Perhaps most notably, and in contrast to the original RT model, the changes in the thalamic error index with changes in the DBS frequency matched well the changes in clinical symptoms with changes in DBS frequency.
62.  Basket cell extrasynaptic inhibition modulates network oscillations (Proddutur et al., 2013)
Among the rhythmic firing patterns observed in brain, gamma oscillations, which are involved in memory formation and retrieval, are generated by networks of fast-spiking basket cells (FS-BCs) with robust interconnectivity through fast GABA synapses. Recently, we identified presence of extrasynaptic tonic GABA currents in FS-BCs and showed that experimentally-induced seizures enhance extrasynaptic tonic GABA currents and render GABA reversal potential (EGABA) depolarizing (Yu et al., 2013). Extrasynaptic GABA currents are mediated by extra- and peri-synaptically located GABAARs and can contribute to synaptic decay kinetics. Additionally, shunting rather than hyperpolarizing EGABA has been shown to increase the frequency and reduce coherence of network oscillations. Using homogeneous networks of biophysically-based, multi-compartmental model FS-BCs, we examined how the presence of extrasynaptic GABA currents and the experimentally identified seizure-induced alterations in GABA currents and EGABA modify the frequency and coherence of network firing.
63.  Biologically-plausible models for spatial navigation (Cannon et al 2003)
Hypotheses about how parahippocampal and hippocampal structures may be involved in spatial navigation tasks are implemented in a model of a virtual rat navigating through a virtual environment in search of a food reward. The model incorporates theta oscillations to separate encoding from retrieval and yields testable predictions about the phase relations of spiking activity to theta oscillations in different parts of the hippocampal formation at various stages of the behavioral task. See paper for more and details.
64.  Biophysically detailed model of the mouse sino-atrial node cell (Kharche et al. 2011)
This model is developed to study the role of various electrophysiological mechanisms in generating cardiac pacemaking action potentials (APs).The model incorporates membrane ionic currents and intracellular mechanisms contributing to spontaneous mouse SAN APs. The model was validated by testing the functional roles of individual membrane currents in one and multiple parameter analyses.The roles of intracellular Ca2+-handling mechanisms on cardiac pacemaking were also investigated in the model.
65.  Biophysically realistic neural modeling of the MEG mu rhythm (Jones et al. 2009)
"Variations in cortical oscillations in the alpha (7–14 Hz) and beta (15–29 Hz) range have been correlated with attention, working memory, and stimulus detection. The mu rhythm recorded with magnetoencephalography (MEG) is a prominent oscillation generated by Rolandic cortex containing alpha and beta bands. Despite its prominence, the neural mechanisms regulating mu are unknown. We characterized the ongoing MEG mu rhythm from a localized source in the finger representation of primary somatosensory (SI) cortex. Subjects showed variation in the relative expression of mu-alpha or mu-beta, which were nonoverlapping for roughly 50% of their respective durations on single trials. To delineate the origins of this rhythm, a biophysically principled computational neural model of SI was developed, with distinct laminae, inhibitory and excitatory neurons, and feedforward (FF, representative of lemniscal thalamic drive) and feedback (FB, representative of higher-order cortical drive or input from nonlemniscal thalamic nuclei) inputs defined by the laminar location of their postsynaptic effects. ..."
66.  Boundary effects influence velocity in transverse propagation of cardiac APs (Sperelakis et al 2005)
... earlier experiments were carried out with 2-dimensional sheets of cells: 2 × 3, 3 × 4, and 5 × 5 models (where the first number is the number of parallel chains and the second is the number of cells in each chain). The purpose of the present study was to enlarge the model size to 7 × 7, thus enabling the transverse velocities to be compared in models of different sizes (where all circuit parameters are identical in all models). This procedure should enable the significance of the role of edge (boundary) effects in transverse propagation to be determined. See paper for more and details.
67.  Broadening of activity with flow across neural structures (Lytton et al. 2008)
"Synfire chains have long been suggested as a substrate for perception and information processing in the nervous system. However, embedding activation chains in a densely connected nervous matrix risks spread of signal that will obscure or obliterate the message. We used computer modeling and physiological measurements in rat hippocampus to assess this problem of activity broadening. We simulated a series of neural modules with feedforward propagation and random connectivity within each module and from one module to the next. ..."
68.  Bursting activity of neuron R15 in Aplysia (Canavier et al 1991, Butera et al 1995)
An equivalent circuit model of the R15 bursting neuron in Aplysia has been combined with a fluid compartment model, resulting in a model that incorporates descriptions of most of the membrane ion channels that are known to exist in the somata of R15, as well as providing a Ca2+ balance on the cell. ... (from the second paper) we have implemented proposed mechanisms for the modulation of two ionic currents (IR and ISI) that play key roles in regulating its spontaneous electrical activity. The model was sufficient to simulate a wide range of endogenous activity in the presence of various concentrations of 5-HT or DA. See papers for more and details.
69.  Bursting and oscillations in RD1 Retina driven by AII Amacrine Neuron (Choi et al. 2014)
"In many forms of retinal degeneration, photoreceptors die but inner retinal circuits remain intact. In the rd1 mouse, an established model for blinding retinal diseases, spontaneous activity in the coupled network of AII amacrine and ON cone bipolar cells leads to rhythmic bursting of ganglion cells. Since such activity could impair retinal and/or cortical responses to restored photoreceptor function, understanding its nature is important for developing treatments of retinal pathologies. Here we analyzed a compartmental model of the wild-type mouse AII amacrine cell to predict that the cell's intrinsic membrane properties, specifically, interacting fast Na and slow, M-type K conductances, would allow its membrane potential to oscillate when light-evoked excitatory synaptic inputs were withdrawn following photoreceptor degeneration. ..."
70.  Bursting and resonance in cerebellar granule cells (D'Angelo et al. 2001)
In this study we report theta-frequency (3-12 Hz) bursting and resonance in rat cerebellar granule cells and show that these neurons express a previously unidentified slow repolarizing K1 current (IK-slow ). Our experimental and modeling results indicate that IK-slow was necessary for both bursting and resonance. See paper for more.
71.  Bursting in dopamine neurons (Li YX et al 1996)
"Burst firing of dopaminergic neurons of the substantia nigra pars compacta can be induced in vitro by the glutamate agonist N-methyl-D-aspartate. It has been suggested that the interburst hyperpolarization is due to Na+ extrusion by a ouabain-sensitive pump (Johnson et al. (1992) Science 258, 665-667). We formulate and explore a theoretical model, with a minimal number of currents, for this novel mechanism of burst generation. This minimal model is further developed into a more elaborate model based on observations of additional currents and hypotheses about their spatial distribution in dopaminergic neurons ... Responses of the model to a number of electrophysiological and pharmacological stimuli are consistent with known responses observed under similar conditions. ..."
72.  Bursting respiratory net: clustered architecture gives large phase diff`s (Fietkiewicz et al 2011)
Using a previous model of respiratory rhythm generation, we modified the network architecture such that cells can be segregated into two clusters. Cells within a given cluster burst with smaller phase differences than do cells from different clusters. This may explain the large phase differences seen experimentally, as reported in the paper.
73.  Ca(2+) oscillations based on Ca-induced Ca-release (Dupont et al 1991)
We consider a simple, minimal model for signal-induced Ca2+ oscillations based on Ca(2+)-induced Ca2+ release. The model takes into account the existence of two pools of intracellular Ca2+, namely, one sensitive to inositol 1,4,5 trisphosphate (InsP3) whose synthesis is elicited by the stimulus, and one insensitive to InsP3. See paper for more and details.
74.  Ca+/HCN channel-dependent persistent activity in multiscale model of neocortex (Neymotin et al 2016)
"Neuronal persistent activity has been primarily assessed in terms of electrical mechanisms, without attention to the complex array of molecular events that also control cell excitability. We developed a multiscale neocortical model proceeding from the molecular to the network level to assess the contributions of calcium regulation of hyperpolarization-activated cyclic nucleotide-gated (HCN) channels in providing additional and complementary support of continuing activation in the network. ..."
75.  CA1 oriens alveus interneurons: signaling properties (Minneci et al. 2007)
The model supports the experimental findings showing that the dynamic interaction between cells with various firing patterns could differently affect GABAergic signaling, leading to a wide range of interneuronal communication within the hippocampal network.
76.  CA1 pyramidal cell: I_NaP and I_M contributions to somatic bursting (Golomb et al 2006)
To study the mechanisms of bursting, we have constructed a conductance-based, one-compartment model of CA1 pyramidal neurons. In this neuron model, reduced [Ca2+]o is simulated by negatively shifting the activation curve of the persistent Na+ current (INaP), as indicated by recent experimental results. The neuron model accounts, with different parameter sets, for the diversity of firing patterns observed experimentally in both zero and normal [Ca2+]o. Increasing INaP in the neuron model induces bursting and increases the number of spikes within a burst, but is neither necessary nor sufficient for bursting. We show, using fast-slow analysis and bifurcation theory, that the M-type K+ current (IM) allows bursting by shifting neuronal behavior between a silent and a tonically-active state, provided the kinetics of the spike generating currents are sufficiently, though not extremely, fast. We suggest that bursting in CA1 pyramidal cells can be explained by a single compartment *square bursting* mechanism with one slow variable, the activation of IM. See paper for more and details.
77.  CA1 pyramidal cell: reconstructed axonal arbor and failures at weak gap junctions (Vladimirov 2011)
Model of pyramidal CA1 cells connected by gap junctions in their axons. Cell geometry is based on anatomical reconstruction of rat CA1 cell (NeuroMorpho.Org ID: NMO_00927) with long axonal arbor. Model init_2cells.hoc shows failures of second spike propagation in a spike doublet, depending on conductance of an axonal gap junction. Model init_ring.hoc shows that spike failure result in reentrant oscillations of a spike in a loop of axons connected by gap junctions, where one gap junction is weak. The paper shows that in random networks of axons connected by gap junctions, oscillations are driven by single pacemaker loop of axons. The shortest loop, around which a spike can travel, is the most likely pacemaker. This principle allows us to predict the frequency of oscillations from network connectivity and visa versa. We propose that this type of oscillations corresponds to so-called fast ripples in epileptic hippocampus.
78.  CA1 pyramidal cells, basket cells, ripples (Malerba et al 2016)
Model of CA1 pyramidal layer Ripple activity, triggered when receiving current input (to represent CA3 sharp-waves). Cells are Adaptive-Exponential Integrate and Fire neurons, receiving independent OU noise.
79.  CA1 pyramidal neuron network model (Ferguson et al 2015)
From the paper: Figure 4 (1000 cell network) is reproduced by running this brian code. The raster plot and one of the excitatory cell voltage is produced.
80.  CA1 pyramidal neuron: as a 2-layer NN and subthreshold synaptic summation (Poirazi et al 2003)
We developed a CA1 pyramidal cell model calibrated with a broad spectrum of in vitro data. Using simultaneous dendritic and somatic recordings, and combining results for two different response measures (peak vs. mean EPSP), two different stimulus formats (single shock vs. 50 Hz trains), and two different spatial integration conditions (within vs. between-branch summation), we found the cell's subthreshold responses to paired inputs are best described as a sum of nonlinear subunit responses, where the subunits correspond to different dendritic branches. In addition to suggesting a new type of experiment and providing testable predictions, our model shows how conclusions regarding synaptic arithmetic can be influenced by an array of seemingly innocuous experimental design choices.
81.  CA1 pyramidal neuron: effects of Ih on distal inputs (Migliore et al 2004)
NEURON mod files from the paper: M. Migliore, L. Messineo, M. Ferrante Dendritic Ih selectively blocks temporal summation of unsynchronized distal inputs in CA1 pyramidal neurons, J.Comput. Neurosci. 16:5-13 (2004). The model demonstrates how the dendritic Ih in pyramidal neurons could selectively suppress AP generation for a volley of excitatory afferents when they are asynchronously and distally activated.
82.  CA1 pyramidal neuron: h channel-dependent deficit of theta oscill. resonance (Marcelin et al. 2008)
This model was used to confirm and support experimental data suggesting that the neuronal/circuitry changes associated with temporal lobe epilepsy, including Ih-dependent inductive mechanisms, can disrupt hippocampal theta function.
83.  CA1 pyramidal neuron: rebound spiking (Ascoli et al.2010)
The model demonstrates that CA1 pyramidal neurons support rebound spikes mediated by hyperpolarization-activated inward current (Ih), and normally masked by A-type potassium channels (KA). Partial KA reduction confined to one or few branches of the apical tuft may be sufficient to elicit a local spike following a train of synaptic inhibition. These data suggest that the plastic regulation of KA can provide a dynamic switch to unmask post-inhibitory spiking in CA1 pyramidal neurons, further increasing the signal processing power of the CA1 synaptic microcircuitry.
84.  CA1 Pyramidal Neuron: Synaptic Scaling (London, Segev 2001)
London and Segev (2001) discuss location dependent and location independent synaptic scaling in a model CA1 neuron with passive dendrites. The freely available text is followed by a critique by Maggee and Cook who comment that the London and Segev model is accurate and informative and however needs to be augmented by active channels in dendrites. Note: the zip files for this model are stored at the nature neuroscience website - Click above Supplementary Source Code in the readme.html in the model files
85.  CA1 pyramidal neurons: binding properties and the magical number 7 (Migliore et al. 2008)
NEURON files from the paper: Single neuron binding properties and the magical number 7, by M. Migliore, G. Novara, D. Tegolo, Hippocampus, in press (2008). In an extensive series of simulations with realistic morphologies and active properties, we demonstrate how n radial (oblique) dendrites of these neurons may be used to bind n inputs to generate an output signal. The results suggest a possible neural code as the most effective n-ple of dendrites that can be used for short-term memory recollection of persons, objects, or places. Our analysis predicts a straightforward physiological explanation for the observed puzzling limit of about 7 short-term memory items that can be stored by humans.
86.  CA1 pyramidal neurons: effect of external electric field from power lines (Cavarretta et al. 2014)
The paper discusses the effects induced by an electric field at power lines frequency.
87.  CA1 pyramidal neurons: effects of a Kv7.2 mutation (Miceli et al. 2009)
NEURON mod files from the paper: Miceli et al, Neutralization of a unique, negatively-charged residue in the voltage sensor of K(V)7.2 subunits in a sporadic case of benign familial neonatal seizures, Neurobiol Dis., in press (2009). In this paper, the model revealed that the gating changes introduced by a mutation in K(v)7.2 genes encoding for the neuronal KM current in a case of benign familial neonatal seizures, increased cell firing frequency, thereby triggering the neuronal hyperexcitability which underlies the observed neonatal epileptic condition.
88.  Ca2+ oscillations in single astrocytes (Lavrentovich and Hemkin 2008) (python) (Manninen et al 2017)
We tested the reproducibility and comparability of four astrocyte models (Manninen, Havela, Linne, 2017). Model by Lavrentovich and Hemkin (2008) was one of them. We implemented and ran the model by Lavrentovich and Hemkin (2008) using Jupyter Notebook. Model code produces results of Figure 1 in Manninen, Havela, Linne (2017).
89.  Ca2+ Oscillations in Sympathetic neurons (Friel 1995)
" ... This study focuses on caffeine-induced [Ca2+]i oscillations in sympathetic neurons. ... The aim of the study was to understand the mechanism responsible for the oscillations. As a starting point, [Ca2+]i relaxations were examined after membrane depolarization and exposure to caffeine. For both stimuli, post-stimulus relaxations could be described by the sum of two decaying exponential functions, consistent with a one-pool system in which Ca2+ transport between compartments is regulated by linear Ca2+ pumps and leaks. After modifying the store to include a [Ca2+]i-sensitive leak, the model also exhibits oscillations such as those observed experimentally. ... Thus, a one-pool model with a single [Ca2+]i-sensitive Ca2+ permeability is adequate to account for many of the quantitative properties of steady-state [Ca2+]i oscillations in sympathetic neurons. ..."
90.  Ca2+-activated I_CAN and synaptic depression promotes network-dependent oscil. (Rubin et al. 2009)
"... the preBotzinger complex... we present and analyze a mathematical model demonstrating an unconventional mechanism of rhythm generation in which glutamatergic synapses and the short-term depression of excitatory transmission play key rhythmogenic roles. Recurrent synaptic excitation triggers postsynaptic Ca2+- activated nonspecific cation current (ICAN) to initiate a network-wide burst. Robust depolarization due to ICAN also causes voltage-dependent spike inactivation, which diminishes recurrent excitation and thus attenuates postsynaptic Ca2+ accumulation. ..."
91.  CA3 Network Model of Epileptic Activity (Sanjay et. al, 2015)
This computational study investigates how a CA3 neuronal network consisting of pyramidal cells, basket cells and OLM interneurons becomes epileptic when dendritic inhibition to pyramidal cells is impaired due to the dysfunction of OLM interneurons. After standardizing the baseline activity (theta-modulated gamma oscillations), systematic changes are made in the connectivities between the neurons, as a result of step-wise impairment of dendritic inhibition.
92.  CA3 pyramidal neuron (Lazarewicz et al 2002)
The model shows how using a CA1-like distribution of active dendritic conductances in a CA3 morphology results in dendritic initiation of spikes during a burst.
93.  CA3 Pyramidal Neuron (Migliore et al 1995)
Model files from the paper: M. Migliore, E. Cook, D.B. Jaffe, D.A. Turner and D. Johnston, Computer simulations of morphologically reconstructed CA3 hippocampal neurons, J. Neurophysiol. 73, 1157-1168 (1995). Demonstrates how the same cell could be bursting or non bursting according to the Ca-independent conductance densities. Includes calculation of intracellular Calcium. Instructions are provided in the below README file. Contact michele.migliore@pa.ibf.cnr.it if you have any questions about the implementation of the model.
94.  CA3 pyramidal neuron (Safiulina et al. 2010)
In this review some of the recent work carried out in our laboratory concerning the functional role of GABAergic signalling at immature mossy fibres (MF)-CA3 principal cell synapses has been highlighted. To compare the relative strength of CA3 pyramidal cell output in relation to their MF glutamatergic or GABAergic inputs in postnatal development, a realistic model was constructed taking into account the different biophysical properties of these synapses.
95.  CA3 pyramidal neuron: firing properties (Hemond et al. 2008)
In the paper, this model was used to identify how relative differences in K+ conductances, specifically KC, KM, & KD, between cells contribute to the different characteristics of the three types of firing patterns observed experimentally.
96.  Caffeine-induced electrical oscillations in Aplysia neurons (Komendantov, Kononenko 2000)
It has been found that in cultured Aplysia neurons bath applications of 40 mM cafffeine evokes oscillations of the membrane potential with about a 40 mV amplitude with a frequency of 0.2 to 0.5 Hz. The most probable mechanism of these caffeine-induced oscillations is inhibition of voltage-activated outward potassium current and, as can be seen from our mathematical modeling, slowdown of inactivation of inward sodium current. It seems likely that these oscillations have a purely membrane origin. Please see paper for results and details.
97.  Calcium influx during striatal upstates (Evans et al. 2013)
"... To investigate the mechanisms that underlie the relationship between calcium and AP timing, we have developed a realistic biophysical model of a medium spiny neuron (MSN). ... Using this model, we found that either the slow inactivation of dendritic sodium channels (NaSI) or the calcium inactivation of voltage-gated calcium channels (CDI) can cause high calcium corresponding to early APs and lower calcium corresponding to later APs. We found that only CDI can account for the experimental observation that sensitivity to AP timing is dependent on NMDA receptors. Additional simulations demonstrated a mechanism by which MSNs can dynamically modulate their sensitivity to AP timing and show that sensitivity to specifically timed pre- and postsynaptic pairings (as in spike timing-dependent plasticity protocols) is altered by the timing of the pairing within the upstate. …"
98.  Cancelling redundant input in ELL pyramidal cells (Bol et al. 2011)
The paper investigates the property of the electrosensory lateral line lobe (ELL) of the brain of weakly electric fish to cancel predictable stimuli. Electroreceptors on the skin encode all signals in their firing activity, but superficial pyramidal (SP) cells in the ELL that receive this feedforward input do not respond to constant sinusoidal signals. This cancellation putatively occurs using a network of feedback delay lines and burst-induced synaptic plasticity between the delay lines and the SP cell that learns to cancel the redundant input. Biologically, the delay lines are parallel fibres from cerebellar-like granule cells in the eminentia granularis posterior. A model of this network (e.g. electroreceptors, SP cells, delay lines and burst-induced plasticity) was constructed to test whether the current knowledge of how the network operates is sufficient to cancel redundant stimuli.
99.  Cell signaling/ion channel variability effects on neuronal response (Anderson, Makadia, et al. 2015)
" ... We evaluated the impact of molecular variability in the expression of cell signaling components and ion channels on electrophysiological excitability and neuromodulation. We employed a computational approach that integrated neuropeptide receptor-mediated signaling with electrophysiology. We simulated a population of neurons in which expression levels of a neuropeptide receptor and multiple ion channels were simultaneously varied within a physiological range. We analyzed the effects of variation on the electrophysiological response to a neuropeptide stimulus. ..."
100.  CellExcite: an efficient simulation environment for excitable cells (Bartocci et al. 2008)
"We have developed CellExcite, a sophisticated simulation environment for excitable-cell networks. CellExcite allows the user to sketch a tissue of excitable cells, plan the stimuli to be applied during simulation, and customize the diffusion model. CellExcite adopts Hybrid Automata (HA) as the computational model in order to efficiently capture both discrete and continuous excitable-cell behavior."
101.  Cerebellar cortex oscil. robustness from Golgi cell gap jncs (Simoes de Souza and De Schutter 2011)
" ... Previous one-dimensional network modeling of the cerebellar granular layer has been successfully linked with a range of cerebellar cortex oscillations observed in vivo. However, the recent discovery of gap junctions between Golgi cells (GoCs), which may cause oscillations by themselves, has raised the question of how gap-junction coupling affects GoC and granular-layer oscillations. To investigate this question, we developed a novel two-dimensional computational model of the GoC-granule cell (GC) circuit with and without gap junctions between GoCs. ..."
102.  Cerebellar gain and timing control model (Yamazaki & Tanaka 2007)(Yamazaki & Nagao 2012)
This paper proposes a hypothetical computational mechanism for unified gain and timing control in the cerebellum. The hypothesis is justified by computer simulations of a large-scale spiking network model of the cerebellum.
103.  Cerebellar Golgi cell (Solinas et al. 2007a, 2007b)
"... Our results suggest that a complex complement of ionic mechanisms is needed to fine-tune separate aspects of the neuronal response dynamics. Simulations also suggest that the Golgi cell may exploit these mechanisms to obtain a fine regulation of timing of incoming mossy fiber responses and granular layer circuit oscillation and bursting."
104.  Cerebellar granular layer (Maex and De Schutter 1998)
Circuit model of the granular layer representing a one-dimensional array of single-compartmental granule cells (grcs) and Golgi cells (Gocs). This paper examines the effects of feedback inhibition (grc -> Goc -> grc) versus feedforward inhibition (mossy fibre -> Goc -> grc) on synchronization and oscillatory behaviour.
105.  Cerebellar Nucleus Neuron (Steuber, Schultheiss, Silver, De Schutter & Jaeger, 2010)
This is the GENESIS 2.3 implementation of a multi-compartmental deep cerebellar nucleus (DCN) neuron model with a full dendritic morphology and appropriate active conductances. We generated a good match of our simulations with DCN current clamp data we recorded in acute slices, including the heterogeneity in the rebound responses. We then examined how inhibitory and excitatory synaptic input interacted with these intrinsic conductances to control DCN firing. We found that the output spiking of the model reflected the ongoing balance of excitatory and inhibitory input rates and that changing the level of inhibition performed an additive operation. Rebound firing following strong Purkinje cell input bursts was also possible, but only if the chloride reversal potential was more negative than -70 mV to allow de-inactivation of rebound currents. Fast rebound bursts due to T-type calcium current and slow rebounds due to persistent sodium current could be differentially regulated by synaptic input, and the pattern of these rebounds was further influenced by HCN current. Our findings suggest that active properties of DCN neurons could play a crucial role for signal processing in the cerebellum.
106.  Cerebellar purkinje cell (De Schutter and Bower 1994)
Tutorial simulation of a cerebellar Purkinje cell. This tutorial is based upon a GENESIS simulation of a cerebellar Purkinje cell, modeled and fine-tuned by Erik de Schutter. The tutorial assumes that you have a basic knowledge of the Purkinje cell and its synaptic inputs. It gives visual insight in how different properties as concentrations and channel conductances vary and interact within a real Purkinje cell.
107.  Cerebellar purkinje cell: interacting Kv3 and Na currents influence firing (Akemann, Knopfel 2006)
Purkinje neurons spontaneously generate action potentials in the absence of synaptic drive and thereby exert a tonic, yet plastic, input to their target cells in the deep cerebellar nuclei. Purkinje neurons express two ionic currents with biophysical properties that are specialized for high-frequency firing: resurgent sodium currents and potassium currents mediated by Kv3.3. Numerical simulations indicated that Kv3.3 increases the spontaneous firing rate via cooperation with resurgent sodium currents. We conclude that the rate of spontaneous action potential firing of Purkinje neurons is controlled by the interaction of Kv3.3 potassium currents and resurgent sodium currents. See paper for more and details.
108.  Cerebellar purkinje cell: K and Ca channels regulate APs (Miyasho et al 2001)
We adopted De Schutter and Bower's model as the starting point, then modified the descriptions of several ion channels, such as the P-type Ca channel and the delayed rectifier K channel, and added class-E Ca channels and D-type K channels to the model. Our new model reproduces most of our experimental results and supports the conclusions of our experimental study that class-E Ca channels and D-type K channels are present and functioning in the dendrites of Purkinje neurons.
109.  Cerebellar Purkinje Cell: resurgent Na current and high frequency firing (Khaliq et al 2003)
These mod files supplied by Dr Raman are for the below two references. ... we modeled action potential firing by simulating eight currents directly recorded from Purkinje cells in both wild-type and (mutant) med mice. Regular, high-frequency firing was slowed in med Purkinje neurons. In addition to disrupted sodium currents, med neurons had small but significant changes in potassium and leak currents. Simulations indicated that these modified non-sodium currents could not account for the reduced excitability of med cells but instead slightly facilitated spiking. The loss of NaV1.6-specific kinetics, however, slowed simulated spontaneous activity. Together, the data suggest that across a range of conditions, sodium currents with a resurgent component promote and accelerate firing. See papers for more and details.
110.  Changes of ionic concentrations during seizure transitions (Gentiletti et al. 2016)
"... In order to investigate the respective roles of synaptic interactions and nonsynaptic mechanisms in seizure transitions, we developed a computational model of hippocampal cells, involving the extracellular space, realistic dynamics of Na+, K+, Ca2+ and Cl - ions, glial uptake and extracellular diffusion mechanisms. We show that the network behavior with fixed ionic concentrations may be quite different from the neurons’ behavior when more detailed modeling of ionic dynamics is included. In particular, we show that in the extended model strong discharge of inhibitory interneurons may result in long lasting accumulation of extracellular K+, which sustains the depolarization of the principal cells and causes their pathological discharges. ..."
111.  Channel density variability among CA1 neurons (Migliore et al. 2018)
The peak conductance of many ion channel types measured in any given animal is highly variable across neurons, both within and between neuronal populations. The current view is that this occurs because a neuron needs to adapt its intrinsic electrophysiological properties either to maintain the same operative range in the presence of abnormal inputs or to compensate for the effects of pathological conditions. Limited experimental and modeling evidence suggests this might be implemented via the correlation and/or degeneracy in the function of multiple types of conductances. To study this mechanism in hippocampal CA1 neurons and interneurons, we systematically generated a set of morphologically and biophysically accurate models. We then analyzed the ensembles of peak conductance obtained for each model neuron. The results suggest that the set of conductances expressed in the various neuron types may be divided into two groups: one group is responsible for the major characteristics of the firing behavior in each population and the other more involved with degeneracy. These models provide experimentally testable predictions on the combination and relative proportion of the different conductance types that should be present in hippocampal CA1 pyramidal cells and interneurons.
112.  Circadian clock model based on protein sequestration (simple version) (Kim & Forger 2012)
"… To understand the biochemical mechanisms of this timekeeping, we have developed a detailed mathematical model of the mammalian circadian clock. Our model can accurately predict diverse experimental data including the phenotypes of mutations or knockdown of clock genes as well as the time courses and relative expression of clock transcripts and proteins. Using this model, we show how a universal motif of circadian timekeeping, where repressors tightly bind activators rather than directly binding to DNA, can generate oscillations when activators and repressors are in stoichiometric balance. …"
113.  Circadian clock model in mammals (detailed version) (Kim & Forger 2012)
"… To understand the biochemical mechanisms of this timekeeping, we have developed a detailed mathematical model of the mammalian circadian clock. Our model can accurately predict diverse experimental data including the phenotypes of mutations or knockdown of clock genes as well as the time courses and relative expression of clock transcripts and proteins. Using this model, we show how a universal motif of circadian timekeeping, where repressors tightly bind activators rather than directly binding to DNA, can generate oscillations when activators and repressors are in stoichiometric balance. …"
114.  Circadian clock model in mammals (PK/PD model) (Kim & Forger 2013)
A systems pharmacology model of the mammalian circadian clock including PF-670462 (CK1d/e inhibitor).
115.  Classic model of the Tritonia Swim CPG (Getting, 1989)
Classic model developed by Petter Getting of the 3-cell core CPG (DSI, C2, and VSI-B) mediating escape swimming in Tritonia diomedea. Cells use a hybrid integrate-and-fire scheme pioneered by Peter Getting. Each model cell is reconstructed from extensive physiological measurements to precisely mimic I-F curves, synaptic waveforms, and functional connectivity. **However, continued physiological measurements show that Getting may have inadvertently incorporated modulatory and or polysynaptic effects -- the properties of this model do *not* match physiological measurements in rested preparations.** This simulation reconstructs the Getting model as reported in: Getting (1989) 'Reconstruction of small neural networks' In Methods in Neural Modeling, 1st ed, p. 171-196. See also, an earlier version of this model reported in Getting (1983). Every attempt has been made to replicate the 1989 model as precisely as possible.
116.  CN bushy, stellate neurons (Rothman, Manis 2003)
Using kinetic data from three different K+ currents in acutely isolated neurons, a single electrical compartment model representing the soma of a ventral cochlear nucleus (VCN) neuron was created. The K+ currents include a fast transient current (IA), a slow-inactivating low-threshold current (ILT), and a noninactivating high-threshold current (IHT). The model also includes a fast-inactivating Na+ current, a hyperpolarization-activated cation current (Ih), and 1-50 auditory nerve synapses. With this model, the role IA, ILT, and IHT play in shaping the discharge patterns of VCN cells is explored. Simulation results indicate these currents have specific roles in shaping the firing patterns of stellate and bushy CN cells. (see readme.txt and the papers, esp 2003c, for details). Any questions regarding these implementations should be directed to: pmanis@med.unc.edu 2 April 2004 Paul B Manis, Ph.D.
117.  CN bushy, stellate neurons (Rothman, Manis 2003) (Brian 2)
This model is an updated version of Romain Brette's adaptation of Rothman & Manis (2003). The model now uses Brian 2 instead of Brian 1 and can be configured to use n cells instead of a single cell. The included figure shows that Brian 2 is more efficient than Brian 1 once the number of cells exceeds 1,000.
118.  CN bushy, stellate neurons (Rothman, Manis 2003) (Brian)
Cochlear neuron model of Rothman & Manis (2003). Adapted from the Neuron implementation.
119.  CN pyramidal fusiform cell (Kanold, Manis 2001)
Pyramidal cells in the dorsal cochlear nucleus (DCN) show three characteristic discharge patterns in response tones: pauser, buildup, and regular firing. Experimental evidence suggests that a rapidly inactivating K+ current (I(KIF)) plays a critical role in generating these discharge patterns. To explore the role of I(KIF), we used a computational model based on the biophysical data. The model replicated the dependence of the discharge pattern on the magnitude and duration of hyperpolarizing prepulses, and I(KIF) was necessary to convey this dependence. Experimentally, half-inactivation voltage and kinetics of I(KIF) show wide variability. Varying these parameters in the model ... suggests that pyramidal cells can adjust their sensitivity to different temporal patterns of inhibition and excitation by modulating the kinetics of I(KIF). Overall, I(KIF) is a critical conductance controlling the excitability of DCN pyramidal cells. (See readme.txt and paper for details). Any questions regarding these implementations should be directed to: pmanis@med.unc.edu 2 April 2004 Paul B Manis, Ph.D.
120.  Code to calc. spike-trig. ave (STA) conduct. from Vm (Pospischil et al. 2007, Rudolph et al. 2007)
PYTHON code to calculate spike-triggered average (STA) conductances from intracellular recordings, according to the method published by Pospischil et al., J Neurophysiol, 2007. The method consists of a maximum likelihood estimate of the conductance STA, from the voltage STA (which is calculated from the data). The method was tested using models and dynamic-clamp experiments; for details, see the original publication (Pospischil et al., 2007). The first application of this method to experimental data was from intracellular recordings in awake cat cerebral cortex (Rudolph et al., 2007).
121.  Collection of simulated data from a thalamocortical network model (Glabska, Chintaluri, Wojcik 2017)
"A major challenge in experimental data analysis is the validation of analytical methods in a fully controlled scenario where the justification of the interpretation can be made directly and not just by plausibility. ... One solution is to use simulations of realistic models to generate ground truth data. In neuroscience, creating such data requires plausible models of neural activity, access to high performance computers, expertise and time to prepare and run the simulations, and to process the output. To facilitate such validation tests of analytical methods we provide rich data sets including intracellular voltage traces, transmembrane currents, morphologies, and spike times. ... The data were generated using the largest publicly available multicompartmental model of thalamocortical network (Traub et al. 2005), with activity evoked by different thalamic stimuli."
122.  Comparison of full and reduced globus pallidus models (Hendrickson 2010)
In this paper, we studied what features of realistic full model activity patterns can and cannot be preserved by morphologically reduced models. To this end, we reduced the morphological complexity of a full globus pallidus neuron model possessing active dendrites and compared its spontaneous and driven responses to those of the reduced models.
123.  Competition for AP initiation sites in a circuit controlling simple learning (Cruz et al. 2007)
"The spatial and temporal patterns of action potential initiations were studied in a behaving leech preparation to determine the basis of increased firing that accompanies sensitization, a form of non-associative learning requiring the S-interneurons. ... The S-interneurons, one in each ganglion and linked by electrical synapses with both neighbors to form a chain, are interposed between sensory and motor neurons. ... the single site with the largest initiation rate, the S-cell in the stimulated segment, suppressed initiations in adjacent ganglia. Experiments showed this was both because (1) it received the earliest, greatest input and (2) the delayed synaptic input to the adjacent S-cells coincided with the action potential refractory period. A compartmental model of the S-cell and its inputs showed that a simple, intrinsic mechanism of inexcitability after each action potential may account for suppression of impulse initiations. Thus, a non-synaptic competition between neurons alters synaptic integration in the chain. In one mode, inputs to different sites sum independently, whereas in another, synaptic input to a single site precisely specifies the overall pattern of activity."
124.  Composite spiking network/neural field model of Parkinsons (Kerr et al 2013)
This code implements a composite model of Parkinson's disease (PD). The composite model consists of a leaky integrate-and-fire spiking neuronal network model being driven by output from a neural field model (instead of the more usual white noise drive). Three different sets of parameters were used for the field model: one with basal ganglia parameters based on data from healthy individuals, one based on data from individuals with PD, and one purely thalamocortical model. The aim of this model is to explore how the different dynamical patterns in each each of these field models affects the activity in the network model.
125.  Computational analysis of NN activity and spatial reach of sharp wave-ripples (Canakci et al 2017)
Network oscillations of different frequencies, durations and amplitudes are hypothesized to coordinate information processing and transfer across brain areas. Among these oscillations, hippocampal sharp wave-ripple complexes (SPW-Rs) are one of the most prominent. SPW-Rs occurring in the hippocampus are suggested to play essential roles in memory consolidation as well as information transfer to the neocortex. To-date, most of the knowledge about SPW-Rs comes from experimental studies averaging responses from neuronal populations monitored by conventional microelectrodes. In this work, we investigate spatiotemporal characteristics of SPW-Rs and how microelectrode size and distance influence SPW-R recordings using a biophysical model of hippocampus. We also explore contributions from neuronal spikes and synaptic potentials to SPW-Rs based on two different types of network activity. Our study suggests that neuronal spikes from pyramidal cells contribute significantly to ripples while high amplitude sharp waves mainly arise from synaptic activity. Our simulations on spatial reach of SPW-Rs show that the amplitudes of sharp waves and ripples exhibit a steep decrease with distance from the network and this effect is more prominent for smaller area electrodes. Furthermore, the amplitude of the signal decreases strongly with increasing electrode surface area as a result of averaging. The relative decrease is more pronounced when the recording electrode is closer to the source of the activity. Through simulations of field potentials across a high-density microelectrode array, we demonstrate the importance of finding the ideal spatial resolution for capturing SPW-Rs with great sensitivity. Our work provides insights on contributions from spikes and synaptic potentials to SPW-Rs and describes the effect of measurement configuration on LFPs to guide experimental studies towards improved SPW-R recordings.
126.  Computational aspects of feedback in neural circuits (Maass et al 2006)
It had previously been shown that generic cortical microcircuit models can perform complex real-time computations on continuous input streams, provided that these computations can be carried out with a rapidly fading memory. We investigate ... the computational capability of such circuits in the more realistic case where not only readout neurons, but in addition a few neurons within the circuit have been trained for specific tasks. This is essentially equivalent to the case where the output of trained readout neurons is fed back into the circuit. We show that this new model overcomes the limitation of a rapidly fading memory. In fact, we prove that in the idealized case without noise it can carry out any conceiv- able digital or analog computation on time-varying inputs. But even with noise the resulting computational model can perform a large class of biologically relevant real-time computations that require a non-fading memory. ... In particular we show that ... generic cortical microcircuits with feedback provide a new model for working memory that is consistent with a large set of biological constraints. See paper for more and details.
127.  Computational Model of a Central Pattern Generator (Cataldo et al 2006)
The buccal ganglia of Aplysia contain a central pattern generator (CPG) that mediates rhythmic movements of the foregut during feeding. This CPG is a multifunctional circuit and generates at least two types of buccal motor patterns (BMPs), one that mediates ingestion (iBMP) and another that mediates rejection (rBMP). The present study used a computational approach to examine the ways in which an ensemble of identified cells and synaptic connections function as a CPG. Hodgkin-Huxley-type models were developed that mimicked the biophysical properties of these cells and synaptic connections. The results suggest that the currently identified ensemble of cells is inadequate to produce rhythmic neural activity and that several key elements of the CPG remain to be identified.
128.  Computer model of clonazepam`s effect in thalamic slice (Lytton 1997)
Demonstration of the effect of a minor pharmacological synaptic change at the network level. Clonazepam, a benzodiazepine, enhances inhibition but is paradoxically useful for certain types of seizures. This simulation shows how inhibition of inhibitory cells (the RE cells) produces this counter-intuitive effect.
129.  Computer models of corticospinal neurons replicate in vitro dynamics (Neymotin et al. 2017)
"Corticospinal neurons (SPI), thick-tufted pyramidal neurons in motor cortex layer 5B that project caudally via the medullary pyramids, display distinct class-specific electrophysiological properties in vitro: strong sag with hyperpolarization, lack of adaptation, and a nearly linear frequency-current (FI) relationship. We used our electrophysiological data to produce a pair of large archives of SPI neuron computer models in two model classes: 1. Detailed models with full reconstruction; 2. Simplified models with 6 compartments. We used a PRAXIS and an evolutionary multiobjective optimization (EMO) in sequence to determine ion channel conductances. ..."
130.  Computing with neural synchrony (Brette 2012)
"... In a heterogeneous neural population, it appears that synchrony patterns represent structure or sensory invariants in stimuli, which can then be detected by postsynaptic neurons. The required neural circuitry can spontaneously emerge with spike-timing-dependent plasticity. Using examples in different sensory modalities, I show that this allows simple neural circuits to extract relevant information from realistic sensory stimuli, for example to identify a fluctuating odor in the presence of distractors. ..."
131.  Continuous lateral oscillations as a mechanism for taxis in Drosophila larvae (Wystrach et al 2016)
" ...Our analysis of larvae motion reveals a rhythmic, continuous lateral oscillation of the anterior body, encompassing all head-sweeps, small or large, without breaking the oscillatory rhythm. Further, we show that an agent-model that embeds this hypothesis reproduces a surprising number of taxis signatures observed in larvae. Also, by coupling the sensory input to a neural oscillator in continuous time, we show that the mechanism is robust and biologically plausible. ..."
132.  Contrast invariance by LGN synaptic depression (Banitt et al. 2007)
"Simple cells in layer 4 of the primary visual cortex of the cat show contrast-invariant orientation tuning, in which the amplitude of the peak response is proportional to the stimulus contrast but the width of the tuning curve hardly changes with contrast. This study uses a detailed model of spiny stellate cells (SSCs) from cat area 17 to explain this property. The model integrates our experimental data, including morphological and intrinsic membrane properties and the number and spatial distribution of four major synaptic input sources of the SSC: the dorsal lateral geniculate nucleus (dLGN) and three cortical sources. ... The model response is in close agreement with experimental results, in terms of both output spikes and membrane voltage (amplitude and fluctuations), with reasonable exceptions given that recurrent connections were not incorporated."
133.  Control of vibrissa motoneuron firing (Harish and Golomb 2010)
We construct and analyze a single-compartment, conductance-based model of vibrissa motoneurons. Low firing rates are supported in extended regimes by adaptation currents and the minimal firing rate decreases with the persistent sodium conductance gNaP and increases with M-potassium and h-cation conductances. Suprathreshold resonance results from the locking properties of vMN firing to stimuli and from reduction of firing rates at low frequencies by slow M and afterhyperpolarization potassium conductances. h conductance only slightly affects the suprathreshold resonance. When a vMN is subjected to a small periodic CPG input, serotonergically induced gNaP elevation may transfer the system from quiescence to a firing state that is highly locked to the CPG input.
134.  Convergence regulates synchronization-dependent AP transfer in feedforward NNs (Sailamul et al 2017)
We study how synchronization-dependent spike transfer can be affected by the structure of convergent feedforward wiring. We implemented computer simulations of model neural networks: a source and a target layer connected with different types of convergent wiring rules. In the Gaussian-Gaussian (GG) model, both the connection probability and the strength are given as Gaussian distribution as a function of spatial distance. In the Uniform-Constant (UC) and Uniform-Exponential (UE) models, the connection probability density is a uniform constant within a certain range, but the connection strength is set as a constant value or an exponentially decaying function, respectively. Then we examined how the spike transfer function is modulated under these conditions, while static or synchronized input patterns were introduced to simulate different levels of feedforward spike synchronization. We observed that the synchronization-dependent modulation of the transfer function appeared noticeably different for each convergence condition. The modulation of the spike transfer function was largest in the UC model, and smallest in the UE model. Our analysis showed that this difference was induced by the different spike weight distributions that was generated from convergent synapses in each model. Our results suggest that the structure of the feedforward convergence is a crucial factor for correlation-dependent spike control, thus must be considered important to understand the mechanism of information transfer in the brain.
135.  Cortex-Basal Ganglia-Thalamus network model (Kumaravelu et al. 2016)
" ... We developed a biophysical network model comprising of the closed loop cortical-basal ganglia-thalamus circuit representing the healthy and parkinsonian rat brain. The network properties of the model were validated by comparing responses evoked in basal ganglia (BG) nuclei by cortical (CTX) stimulation to published experimental results. A key emergent property of the model was generation of low-frequency network oscillations. Consistent with their putative pathological role, low-frequency oscillations in model BG neurons were exaggerated in the parkinsonian state compared to the healthy condition. ..."
136.  Cortical oscillations and the basal ganglia (Fountas & Shanahan 2017)
"Although brain oscillations involving the basal ganglia (BG) have been the target of extensive research, the main focus lies disproportionally on oscillations generated within the BG circuit rather than other sources, such as cortical areas. We remedy this here by investigating the influence of various cortical frequency bands on the intrinsic effective connectivity of the BG, as well as the role of the latter in regulating cortical behaviour. To do this, we construct a detailed neural model of the complete BG circuit based on fine-tuned spiking neurons, with both electrical and chemical synapses as well as short-term plasticity between structures. As a measure of effective connectivity, we estimate information transfer between nuclei by means of transfer entropy. Our model successfully reproduces firing and oscillatory behaviour found in both the healthy and Parkinsonian BG. We found that, indeed, effective connectivity changes dramatically for different cortical frequency bands and phase offsets, which are able to modulate (or even block) information flow in the three major BG pathways. ..."
137.  Current Dipole in Laminar Neocortex (Lee et al. 2013)
Laminar neocortical model in NEURON/Python, adapted from Jones et al 2009. https://bitbucket.org/jonescompneurolab/corticaldipole
138.  D2 dopamine receptor modulation of interneuronal activity (Maurice et al. 2004)
"... Using a combination of electrophysiological, molecular, and computational approaches, the studies reported here show that D2 dopamine receptor modulation of Na+ currents underlying autonomous spiking contributes to a slowing of discharge rate, such as that seen in vivo. Four lines of evidence support this conclusion. ... Fourth, simulation of cholinergic interneuron pacemaking revealed that a modest increase in the entry of Na+ channels into the slow-inactivated state was sufficient to account for the slowing of pacemaker discharge. These studies establish a cellular mechanism linking dopamine and the reduction in striatal cholinergic interneuron activity seen in the initial stages of associative learning." See paper for more and details.
139.  Data-driven, HH-type model of the lateral pyloric (LP) cell in the STG (Nowotny et al. 2008)
This model was developed using voltage clamp data and existing LP models to assemble an initial set of currents which were then adjusted by extensive fitting to a long data set of an isolated LP neuron. The main points of the work are a) automatic fitting is difficult but works when the method is carefully adjusted to the problem (and the initial guess is good enough). b) The resulting model (in this case) made reasonable predictions for manipulations not included in the original data set, e.g., blocking some of the ionic currents. c) The model is reasonably robust against changes in parameters but the different parameters vary a lot in this respect. d) The model is suitable for use in a network and has been used for this purpose (Ivanchenko et al. 2008)
140.  Dendritic Discrimination of Temporal Input Sequences (Branco et al. 2010)
Compartmental model of a layer 2/3 pyramidal cell in the rat somatosensory cortex, exploring NMDA-dependent sensitivity to the temporal sequence of synaptic activation.
141.  Dendritic L-type Ca currents in motoneurons (Carlin et al 2000)
A component of recorded currents demonstrated kinetics consistent with a current originating at a site spatially segregated from the soma. In response to step commands this component was seen as a late-onset, low amplitude persistent current whilst in response to depolarizing-repolarizing ramp commands a low voltage clockwise current hysteresis was recorded. Simulations using a neuromorphic motoneuron model could reproduce these currents only if a noninactivating calcium conductance was placed in the dendritic compartments.
142.  Dendritic Na inactivation drives a decrease in ISI (Fernandez et al 2005)
We use a combination of dynamical analysis and electrophysiological recordings to demonstrate that spike broadening in dendrites is primarily caused by a cumulative inactivation of dendritic Na(+) current. We further show that a reduction in dendritic Na(+) current increases excitability by decreasing the interspike interval (ISI) and promoting burst firing.
143.  Dendritica (Vetter et al 2001)
Dendritica is a collection of programs for relating dendritic geometry and signal propagation. The programs are based on those used for the simulations described in: Vetter, P., Roth, A. & Hausser, M. (2001) For reprint requests and additional information please contact Dr. M. Hausser, email address: m.hausser@ucl.ac.uk
144.  Dentate Gyrus Feed-forward inhibition (Ferrante et al. 2009)
In this paper, the model was used to show how that FFI can change a steeply sigmoidal input-output (I/O) curve into a double-sigmoid typical of buffer systems.
145.  Dentate gyrus granule cell: calcium and calcium-dependent conductances (Aradi and Holmes 1999)
We have constructed a detailed model of a hippocampal dentate granule (DG) cell that includes nine different channel types. Channel densities and distributions were chosen to reproduce reported physiological responses observed in normal solution and when blockers were applied. The model was used to explore the contribution of each channel type to spiking behavior with particular emphasis on the mechanisms underlying postspike events. ... The model was used to predict changes in channel densities that could lead to epileptogenic burst discharges and to predict the effect of altered buffering capacity on firing behavior. We conclude that the clustered spatial distributions of calcium related channels, the presence of slow delayed rectifier potassium currents in dendrites, and calcium buffering properties, together, might explain the resistance of DG cells to the development of epileptogenic burst discharges.
146.  Dentate gyrus network model (Santhakumar et al 2005)
Mossy cell loss and mossy fiber sprouting are two characteristic consequences of repeated seizures and head trauma. However, their precise contributions to the hyperexcitable state are not well understood. Because it is difficult, and frequently impossible, to independently examine using experimental techniques whether it is the loss of mossy cells or the sprouting of mossy fibers that leads to dentate hyperexcitability, we built a biophysically realistic and anatomically representative computational model of the dentate gyrus to examine this question. The 527-cell model, containing granule, mossy, basket, and hilar cells with axonal projections to the perforant-path termination zone, showed that even weak mossy fiber sprouting (10-15% of the strong sprouting observed in the pilocarpine model of epilepsy) resulted in the spread of seizure-like activity to the adjacent model hippocampal laminae after focal stimulation of the perforant path. See reference for more and details.
147.  Dentate gyrus network model (Tejada et al 2014)
" ... Here we adapted an existing computational model of the dentate gyrus (J Neurophysiol 93: 437-453, 2005) by replacing the reduced granule cell models with morphologically detailed models coming from (3D) reconstructions of mature cells. ... Different fractions of the mature granule cell models were replaced by morphologically reconstructed models of newborn dentate granule cells from animals with PILO-induced Status Epilepticus, which have apical dendritic alterations and spine loss, and control animals, which do not have these alterations. This complex arrangement of cells and processes allowed us to study the combined effect of mossy fiber sprouting, altered apical dendritic tree and dendritic spine loss in newborn granule cells on the excitability of the dentate gyrus model. Our simulations suggest that alterations in the apical dendritic tree and dendritic spine loss in newborn granule cells have opposing effects on the excitability of the dentate gyrus after Status Epilepticus. Apical dendritic alterations potentiate the increase of excitability provoked by mossy fiber sprouting while spine loss curtails this increase. "
148.  Dentate gyrus network model pattern separation and granule cell scaling in epilepsy (Yim et al 2015)
The dentate gyrus (DG) is thought to enable efficient hippocampal memory acquisition via pattern separation. With patterns defined as spatiotemporally distributed action potential sequences, the principal DG output neurons (granule cells, GCs), presumably sparsen and separate similar input patterns from the perforant path (PP). In electrophysiological experiments, we have demonstrated that during temporal lobe epilepsy (TLE), GCs downscale their excitability by transcriptional upregulation of ‘leak’ channels. Here we studied whether this cell type-specific intrinsic plasticity is in a position to homeostatically adjust DG network function. We modified an established conductance-based computer model of the DG network such that it realizes a spatiotemporal pattern separation task, and quantified its performance with and without the experimentally constrained leaky GC phenotype. ...
149.  Dependence of neuronal firing on astroglial membrane transport mechanisms (Oyehaug et al 2012)
"Exposed to a sufficiently high extracellular potassium concentration ([K?+?]o), the neuron can fire spontaneous discharges or even become inactivated due to membrane depolarisation (‘depolarisation block’). Since these phenomena likely are related to the maintenance and propagation of seizure discharges, it is of considerable importance to understand the conditions under which excess [K?+?]o causes them. To address the putative effect of glial buffering on neuronal activity under elevated [K?+?]o conditions, we combined a recently developed dynamical model of glial membrane ion and water transport with a Hodgkin–Huxley type neuron model. In this interconnected glia-neuron model we investigated the effects of natural heterogeneity or pathological changes in glial membrane transporter density by considering a large set of models with different, yet empirically plausible, sets of model parameters. ..."
150.  Determinants of the intracellular and extracellular waveforms in DA neurons (Lopez-Jury et al 2018)
To systematically address the contribution of AIS, dendritic and somatic compartments to shaping the two-component action potentials (APs), we modeled APs of male mouse and rat dopaminergic neurons. A parsimonious two-domain model, with high (AIS) and lower (dendro-somatic) Na+ conductance, reproduced the notch in the temporal derivatives, but not in the extracellular APs, regardless of morphology. The notch was only revealed when somatic active currents were reduced, constraining the model to three domains. Thus, an initial AIS spike is followed by an actively generated spike by the axon-bearing dendrite (ABD), in turn followed mostly passively by the soma. Larger AISs and thinner ABD (but not soma-to-AIS distance) accentuate the AIS component.
151.  Deterministic chaos in a mathematical model of a snail neuron (Komendantov and Kononenko 1996)
"Chaotic regimes in a mathematical model of pacemaker activity in the bursting neurons of a snail Helix pomatia, have been investigated. The model includes a slow-wave generating mechanism, a spike-generating mechanism, an inward Ca current, intracellular Ca ions, [Ca2+]in, their fast buffering and uptake by intracellular Ca stores, and a [Ca2+]in-inhibited Ca current. Chemosensitive voltage-activated conductance, gB*, responsible for termination of the spike burst, and chemosensitive sodium conductance, gNa*, responsible for the depolarization phase of the slow-wave, were used as control parameters. ... Time courses of the membrane potential and [Ca2+]in were employed to analyse different regimes in the model. ..."
152.  Differences between type A and B photoreceptors (Blackwell 2006)
In Hermissenda crassicornis, the memory of light associated with turbulence is stored as changes in intrinsic and synaptic currents in both type A and type B photoreceptors. These photoreceptor types exhibit qualitatively different responses to light and current injection, and these differences shape the spatiotemporal firing patterns that control behavior. Thus the objective of the study was to identify the mechanisms underlying these differences. The approach was to develop a type B model that reproduced characteristics of type B photoreceptors recorded in vitro, and then to create a type A model by modifying a select number of ionic currents. Comparison of type A models with characteristics of type A photoreceptors recorded in vitro revealed that type A and type B photoreceptors have five main differences, three that have been characterized experimentally and two that constitute hypotheses to be tested with experiments in the future. See paper for more and details.
153.  Different roles for inhibition in the rhythm-generating respiratory network (Harris et al 2017)
"Unraveling the interplay of excitation and inhibition within rhythm-generating networks remains a fundamental issue in neuroscience. We use a biophysical model to investigate the different roles of local and long-range inhibition in the respiratory network, a key component of which is the pre-Bötzinger complex inspiratory microcircuit. ..."
154.  Differential modulation of pattern and rate in a dopamine neuron model (Canavier and Landry 2006)
"A stylized, symmetric, compartmental model of a dopamine neuron in vivo shows how rate and pattern can be modulated either concurrently or differentially. If two or more parameters in the model are varied concurrently, the baseline firing rate and the extent of bursting become decorrelated, which provides an explanation for the lack of a tight correlation in vivo and is consistent with some independence of the mechanisms that generate baseline firing rates versus bursting. ..." See paper for more and details.
155.  Distance-dependent inhibition in the hippocampus (Strüber et al. 2017)
Network model of a hippocampal circuit including interneurons and principal cells. Amplitude and decay time course of inhibitory synapses can be systematically changed for different distances between connected cells. Various forms of excitatory drives can be administered to the network including spatially structured input.
156.  Distinct current modules shape cellular dynamics in model neurons (Alturki et al 2016)
" ... We hypothesized that currents are grouped into distinct modules that shape specific neuronal characteristics or signatures, such as resting potential, sub-threshold oscillations, and spiking waveforms, for several classes of neurons. For such a grouping to occur, the currents within one module should have minimal functional interference with currents belonging to other modules. This condition is satisfied if the gating functions of currents in the same module are grouped together on the voltage axis; in contrast, such functions are segregated along the voltage axis for currents belonging to different modules. We tested this hypothesis using four published example case models and found it to be valid for these classes of neurons. ..."
157.  Dopamine neuron of the vent. periaqu. gray and dors. raphe nucleus (vlPAG/DRN) (Dougalis et al 2017)
The following computer model describes the electrophysiological properties of dopamine (DA) neurons of the ventrolateral periaquaductal gray and dorsal raphe nucleus (vlPAG/DRN). the model and how to replicate Figures 7-10 of the manuscript (Dougalis et al., 2017 J Comput Neurosci). SUMMARY: We have conducted a voltage-clamp study to provide a kinetic description of major sodium, potassium and calcium ionic currents operant on adult DA vlPAG/DRN neurons in brain slices obtained from pitx3-GFP mice. Based on experimentally derived voltage-clamp data, we then constructed a simplified, conductance-based, Hodgkin and Huxley-type, computer model and validated its behaviour against in vitro neurophysiological data. Using simulations in the computational DA model, we explored the contribution of individual ionic currents in vlPAG/DRN DA neuron’s spontaneous firing, pacemaker frequency and threshold for spike frequency adaptation in silico. The data presented here extend our previous physiological characterization (Dougalis et al. 2012) and argue that DA neurons of the vlPAG/DRN express autorhythmicity in the absence of synaptic transmission via the interplay of potassium and sodium currents without the absolute need of calcium currents. The properties of the ionic currents recorded here (IH current, IA current), the lack of small oscillating potentials in the presence of sodium channel blockers taken together with the mechanisms for autorhythmicity (reliance more on sodium rather than calcium currents) also support further the idea that vlPAG/DRN DA neurons are operationally similar to VTA, rather than SNc, DA neurons. In particular, the properties of a slowly inactivating IA current in conjunction with the small and slowly activating IH current described herein pinpoint that vlPAG/DRN DA neurons are most similar to prefrontal cortex or medial shell of nucleus accumbens projecting DA neurons (see Lammel et al. 2008, 2011).
158.  Dopaminergic cell bursting model (Kuznetsov et al 2006)
Dopaminergic neurons of the midbrain fire spontaneously at rates <10/s and ordinarily will not exceed this range even when driven with somatic current injection. During spontaneous bursting of dopaminergic neurons in vivo, bursts related to reward expectation in behaving animals, and bursts generated by dendritic application of N-methyl-D-aspartate (NMDA) agonists, transient firing attains rates well above this range. We suggest a way such highfrequency firing may occur in response to dendritic NMDA receptor activation. We have extended the coupled oscillator model of the dopaminergic neuron, which represents the soma and dendrites as electrically coupled compartments with different natural spiking frequencies, by addition of dendritic AMPA (voltage-independent) or NMDA (voltage-dependent) synaptic conductance. Both soma and dendrites contain a simplified version of the calcium-potassium mechanism known to be the mechanism for slow spontaneous oscillation and background firing in dopaminergic cells. We show that because of its voltage dependence, NMDA receptor activation acts to amplify the effect on the soma of the high-frequency oscillation of the dendrites, which is normally too weak to exert a large influence on the overall oscillation frequency of the neuron.
159.  Dorsal root ganglion (DRG) neuronal model (Amir, Devor 2003)
The model shows that an electrically excitable soma is not necessary for spike through-conduction in the t-shaped geometry of a dorsal root ganglion neuron axon. Electrical excitability of the soma is required, however, for soma spike invasion. See papers for details and more.
160.  Dorsal root ganglion (DRG) neuronal model (Kovalsky et al. 2009)
This model, diverged from oscillatory parameters seen in live cells and failed to produce characteristic ectopic discharge patterns. Here we show that use of a more complete set of Na+ conductances--which includes several delayed components--enables simulation of the entire repertoire of oscillation-triggered electrogenic phenomena seen in live dorsal root ganglion (DRG) neurons. This includes a physiological window of induction and natural patterns of spike discharge. An INa+ component at 2-20 ms was particularly important, even though it represented only a tiny fraction of overall INa+ amplitude. With the addition of a delayed rectifier IK+ the singlet firing seen in some DRG neurons can also be simulated. The model reveals the key conductances that underlie afferent ectopia, conductances that are potentially attractive targets in the search for more effective treatments of neuropathic pain.
161.  Double boundary value problem (A. Bose and J.E. Rubin, 2015)
For two neurons coupled with mutual inhibition, we investigate the strategies that each neuron should utilize in order to maximize the number of spikes it can fire (or equivalently the amount of time it is active) before the other neuron takes over. We derive a one-dimensional map whose fixed points correspond to periodic anti-phase bursting solutions. The model here solves a novel double boundary value problem that can be used to obtain the graph of this map. Read More: http://www.worldscientific.com/doi/abs/10.1142/S0218127415400040
162.  DRG neuron models investigate how ion channel levels regulate firing properties (Zheng et al 2019)
We present computational models for an Abeta-LTMR (low-threshold mechanoreceptor) and a C-LTMR expressing four Na channels and four K channels to investigate how the expression level of Kv1 and Kv4 regulate number of spikes (repetitive firing) and onset latency to action potentials in Abeta-LTMRs and C-LTMRs, respectively.
163.  DRt neuron model (Sousa et al., 2014)
Despite the importance and significant clinical impact of understanding information processing in the nociceptive system, the functional properties of neurons in many parts of this system are still unknown. In this work we performed whole-cell patch-clamp recording in rat brainstem blocks to characterize the electrophysiological properties of neurons in the dorsal reticular nucleus (DRt), a region known to be involved in pronociceptive modulation. We also compared properties of DRt neurons with those in the adjacent parvicellular reticular nucleus (PCRt) and in neighboring regions outside the reticular formation. We found that neurons in the DRt and PCRt had similar electrophysiological properties and exhibited mostly tonic-like firing patterns, whereas neurons outside the reticular formation showed a larger diversity of firing-patterns. The dominance of tonic neurons in the DRt supports previous conclusions that these neurons encode stimulus intensity through their firing frequency.
164.  Duration-tuned neurons from the inferior colliculus of the big brown bat (Aubie et al. 2009)
dtnet is a generalized neural network simulator written in C++ with an easy to use XML description language to generate arbitrary neural networks and then run simulations covering many different parameter values. For example, you can specify ranges of parameter values for several different connection weights and then automatically run simulations over all possible parameters. Graphing ability is built in as long as the free, open-source, graphing application GLE (http://glx.sourceforge.net/) is installed. Included in the examples folder are simulation descriptions that were used to generate the results in Aubie et al. (2009). Refer to the README file for instructions on compiling and running these examples. The most recent source code can be obtained from GitHub: <a href="https://github.com/baubie/dtnet">https://github.com/baubie/dtnet</a>
165.  Dynamic cortical interlaminar interactions (Carracedo et al. 2013)
"... Here we demonstrate the mechanism underlying a purely neocortical delta rhythm generator and show a remarkable laminar, cell subtype and local subcircuit delineation between delta and nested theta rhythms. We show that spike timing during delta-nested theta rhythms controls an iterative, reciprocal interaction between deep and superficial cortical layers resembling the unsupervised learning processes proposed for laminar neural networks by Hinton and colleagues ... and mimicking the alternating cortical dynamics of sensory and memory processing during wakefulness."
166.  Dynamical model of olfactory bulb mitral cell (Rubin, Cleland 2006)
This four-compartment mitral cell exhibits endogenous subthreshold oscillations, phase resetting, and evoked spike phasing properties as described in electrophysiological studies of mitral cells. It is derived from the prior work of Davison et al (2000) and Bhalla and Bower (1993). See readme.txt for details.
167.  Dynamical patterns underlying response properties of cortical circuits (Keane et al 2018)
"Recent experimental studies show cortical circuit responses to external stimuli display varied dynamical properties. These include stimulus strength-dependent population response patterns, a shift from synchronous to asynchronous states and a decline in neural variability. To elucidate the mechanisms underlying these response properties and explore how they are mechanistically related, we develop a neural circuit model that incorporates two essential features widely observed in the cerebral cortex. The first feature is a balance between excitatory and inhibitory inputs to individual neurons; the second feature is distance-dependent connectivity. We show that applying a weak external stimulus to the model evokes a wave pattern propagating along lateral connections, but a strong external stimulus triggers a localized pattern; these stimulus strength-dependent population response patterns are quantitatively comparable with those measured in experimental studies. ..."
168.  Dynamics of sleep oscillations coupled to brain temperature on multiple scales (Csernai et al 2019)
"Every form of neural activity depends on temperature, yet its relationship to brain rhythms is poorly understood. In this work we examined how sleep spindles are influenced by changing brain temperatures and how brain temperature is influenced by sleep oscillations. We employed a novel thermoelectrode designed for measuring temperature while recording neural activity. We found that spindle frequency is positively correlated and duration negatively correlated with brain temperature. Local heating of the thalamus replicated the temperature dependence of spindle parameters in the heated area only, suggesting biophysical rather than global modulatory mechanisms, a finding also supported by a thalamic network model. Finally, we show that switches between oscillatory states also influence brain temperature on a shorter and smaller scale. Epochs of paradoxical sleep as well as the infra-slow oscillation were associated with brain temperature fluctuations below 0.2°C. Our results highlight that brain temperature is massively intertwined with sleep oscillations on various time scales."
169.  Effect of slowly inactivating IKdr to delayed firing of action potentials (Wu et al. 2008)
"The properties of slowly inactivating delayed-rectifier K+ current (IKdr) were investigated in NG108-15 neuronal cells differentiated with long-term exposure to dibutyryl cyclic AMP. ... The computer model, in which state-dependent inactivation of IKdr was incorporated, was also implemented to predict the firing behavior present in NG108-15 cells. ... Our theoretical work and the experimental results led us to propose a pivotal role of slowly inactivating IKdr in delayed firing of APs in NG108-15 cells. The results also suggest that aconitine modulation of IKdr gating is an important molecular mechanism through which it can contribute to neuronal firing."
170.  Effects of Acetyl-L-carnitine on neural transmission (Lombardo et al 2004)
Acetyl-L-carnitine is known to improve many aspects of the neural activity even if its exact role in neurotransmission is still unknown. This study investigates the effects of acetyl-L-carnitine in T segmental sensory neurons of the leech Hirudo medicinalis. These neurons are involved in some forms of neural plasticity associated with learning processes. Their physiological firing is accompanied by a large afterhyperpolarization that is mainly due to the Na+/K+ ATPase activity and partially to a Ca2+-dependent K+ current. A clear-cut hyperpolarization and a significant increase of the afterhyperpolarization have been recorded in T neurons of leeches injected with 2 mM acetyl-L-carnitine some days before. Acute treatments of 50 mM acetyl-L-carnitine induced similar effects in T cells of naive animals. Moreover, in these cells, widely arborized, the afterhyperpolarization seems to play an important role in determining the action potential transmission at neuritic bifurcations. A computational model of a T cell has been previously developed considering detailed data for geometry and the modulation of the pump current. Herein, we showed that to a larger afterhyperpolarization, due to the acetyl-L-carnitine-induced effects, corresponds a decrement in the number of action potentials reaching synaptic terminals.
171.  Effects of electric fields on cognitive functions (Migliore et al 2016)
The paper discusses the effects induced by an electric field at power lines frequency on neuronal activity during cognitive processes.
172.  Effects of KIR current inactivation in NAc Medium Spiny Neurons (Steephen and Manchanda 2009)
"Inward rectifying potassium (KIR) currents in medium spiny (MS) neurons of nucleus accumbens inactivate significantly in ~40% of the neurons but not in the rest, which may lead to differences in input processing by these two groups. Using a 189-compartment computational model of the MS neuron, we investigate the influence of this property using injected current as well as spatiotemporally distributed synaptic inputs. Our study demonstrates that KIR current inactivation facilitates depolarization, firing frequency and firing onset in these neurons. ..."
173.  Emergence of physiological oscillation frequencies in neocortex simulations (Neymotin et al. 2011)
"Coordination of neocortical oscillations has been hypothesized to underlie the "binding" essential to cognitive function. However, the mechanisms that generate neocortical oscillations in physiological frequency bands remain unknown. We hypothesized that interlaminar relations in neocortex would provide multiple intermediate loops that would play particular roles in generating oscillations, adding different dynamics to the network. We simulated networks from sensory neocortex using 9 columns of event-driven rule-based neurons wired according to anatomical data and driven with random white-noise synaptic inputs. ..."
174.  Emergent properties of networks of biological signaling pathways (Bhalla, Iyengar 1999)
Biochemical signaling networks were constructed with experimentally obtained constants and analyzed by computational methods to understand their role in complex biological processes. These networks exhibit emergent properties such as integration of signals across multiple time scales, generation of distinct outputs depending on input strength and duration, and self-sustaining feedback loops. Properties of signaling networks raise the possibility that information for "learned behavior" of biological systems may be stored within intracellular biochemical reactions that comprise signaling pathways.
175.  Encoding and discrimination of vowel-like sounds (Tan and Carney 2005)
"The sensitivity of listeners to changes in the center frequency of vowel-like harmonic complexes as a function of the center frequency of the complex cannot be explained by changes in the level of the stimulus [Lyzenga and Horst, J. Acoust. Soc. Am. 98, 1943–1955 (1995)]. Rather, a complex pattern of sensitivity is seen; for a spectrum with a triangular envelope, the greatest sensitivity occurs when the center frequency falls between harmonics, whereas for a spectrum with a trapezoidal envelope, greatest sensitivity occurs when the center frequency is aligned with a harmonic. In this study, the thresholds of a population model of auditory-nerve (AN) fibers were quantitatively compared to these trends in psychophysical thresholds. Single-fiber and population model responses were evaluated in terms of both average discharge rate and the combination of rate and timing information. ..."
176.  Encoding and retrieval in a model of the hippocampal CA1 microcircuit (Cutsuridis et al. 2009)
This NEURON code implements a small network model (100 pyramidal cells and 4 types of inhibitory interneuron) of storage and recall of patterns in the CA1 region of the mammalian hippocampus. Patterns of PC activity are stored either by a predefined weight matrix generated by Hebbian learning, or by STDP at CA3 Schaffer collateral AMPA synapses.
177.  Endothelin action on pituitary latotrophs (Bertram et al. 2006)
Endothelin (ET-1, -2, and -3 designate three genes which produce different endothelin isopeptides) is a prolactin inhibiting substance of hypothalmic origin. ET-1 binding is part of at least four G protein signaling pathways in lactotrophs. The sequence of events in these pathways following the presentation of nano- and pico-molar concentrations of ET-1 is modeled in the paper.
178.  Engaging distinct oscillatory neocortical circuits (Vierling-Claassen et al. 2010)
"Selective optogenetic drive of fast-spiking (FS) interneurons (INs) leads to enhanced local field potential (LFP) power across the traditional “gamma” frequency band (20–80 Hz; Cardin et al., 2009). In contrast, drive to regular-spiking (RS) pyramidal cells enhances power at lower frequencies, with a peak at 8 Hz. The first result is consistent with previous computational studies emphasizing the role of FS and the time constant of GABAA synaptic inhibition in gamma rhythmicity. However, the same theoretical models do not typically predict low-frequency LFP enhancement with RS drive. To develop hypotheses as to how the same network can support these contrasting behaviors, we constructed a biophysically principled network model of primary somatosensory neocortex containing FS, RS, and low-threshold spiking (LTS) INs. ..."
179.  Enhanced Excitability in Hermissenda: modulation by 5-HT (Cai et al 2003)
Serotonin (5-HT) applied to the exposed but otherwise intact nervous system results in enhanced excitability of Hermissenda type-B photoreceptors. Several ion currents in the type-B photoreceptors are modulated by 5-HT, including the A-type K+ current (IK,A), sustained Ca2+ current (ICa,S), Ca-dependent K+ current (IK,Ca), and a hyperpolarization-activated inward rectifier current (Ih). In this study,we developed a computational model that reproduces physiological characteristics of type B photoreceptors, e.g. resting membrane potential, dark-adapted spike activity, spike width, and the amplitude difference between somatic and axonal spikes. We then used the model to investigate the contribution of different ion currents modulated by 5-HT to the magnitudes of enhanced excitability produced by 5-HT. See paper for results and more details.
180.  ERG current in repolarizing plateau potentials in dopamine neurons (Canavier et al 2007)
"Blocking the small-conductance (SK) calcium-activated potassium channel promotes burst firing in dopamine neurons both in vivo and in vitro. ... We focus on the underlying plateau potential oscillation generated in the presence of both apamin and TTX, so that action potentials are not considered. We find that although the plateau potentials are mediated by a voltage-gated Ca2+ current, they do not depend on the accumulation of cytosolic Ca2+, then use a computational model to test the hypothesis that the slowly voltage-activated ether-a-go-go–related gene (ERG) potassium current repolarizes the plateaus. The model, which includes a material balance on calcium, is able to reproduce the time course of both membrane potential and somatic calcium concentration, and can also mimic the induction of plateau potentials by the calcium chelator BAPTA." See paper for more.
181.  Escape response latency in the Giant Fiber System of Drosophila melanogastor (Augustin et al 2019)
"The Giant Fiber System (GFS) is a multi-component neuronal pathway mediating rapid escape response in the adult fruit-fly Drosophila melanogaster, usually in the face of a threatening visual stimulus. Two branches of the circuit promote the response by stimulating an escape jump followed by flight initiation. Our recent work demonstrated an age-associated decline in the speed of signal propagation through the circuit, measured as the stimulus-to-muscle depolarization response latency. The decline is likely due to the diminishing number of inter-neuronal gap junctions in the GFS of ageing flies. In this work, we presented a realistic conductance-based, computational model of the GFS that recapitulates our experimental results and identifies some of the critical anatomical and physiological components governing the circuit's response latency. According to our model, anatomical properties of the GFS neurons have a stronger impact on the transmission than neuronal membrane conductance densities. The model provides testable predictions for the effect of experimental interventions on the circuit's performance in young and ageing flies."
182.  Evolving simple models of diverse dynamics in hippocampal neuron types (Venkadesh et al 2018)
" ... we present an automated pipeline based on evolutionary algorithms to quantitatively reproduce features of various classes of neuronal spike patterns using the Izhikevich model. Employing experimental data from Hippocampome.org, a comprehensive knowledgebase of neuron types in the rodent hippocampus, we demonstrate that our approach reliably fit Izhikevich models to nine distinct classes of experimentally recorded spike patterns, including delayed spiking, spiking with adaptation, stuttering, and bursting. ..."
183.  Excessive beta oscillations in Parkinson's disease (Pavlides et al. 2015)
" ... Understanding the generation of beta oscillations is important to improve treatments for Parkinson’s disease. Competing theories exist for how these oscillations are generated in the affected brain circuits, which include the motor cortex and a set of subcortical nuclei called the basal ganglia. This paper suggests two hypotheses for the generation of beta oscillations. The first hypothesis is that beta oscillations are generated in the motor cortex, and the basal ganglia resonate to the cortical input. The second hypothesis additionally proposes that feedback from the basal ganglia to cortex is critically important for the presence of the oscillations. We show that the models can successfully account for a wide range of experimental data concerning the presence of beta oscillations in Parkinson’s disease."
184.  Excitability of DA neurons and their regulation by synaptic input (Morozova et al. 2016a, 2016b)
This code contains conductance-based models of Dopaminergic (DA) and GABAergic neurons, used in Morozova et al 2016 PLOS Computational Biology paper in order to study the type of excitability of the DA neurons and how it is influenced by the intrinsic and synaptic currents. We identified the type of excitability by calculating bifurcation diagrams and F-I curves using XPP file. This model was also used in Morozova et al 2016 J. Neurophysiology paper in order to study the effect of synchronization in GABAergic inputs on the firing dynamics of the DA neuron.
185.  Excitability of PFC Basal Dendrites (Acker and Antic 2009)
".. We carried out multi-site voltage-sensitive dye imaging of membrane potential transients from thin basal branches of prefrontal cortical pyramidal neurons before and after application of channel blockers. We found that backpropagating action potentials (bAPs) are predominantly controlled by voltage-gated sodium and A-type potassium channels. In contrast, pharmacologically blocking the delayed rectifier potassium, voltage-gated calcium or Ih, conductance had little effect on dendritic action potential propagation. Optically recorded bAP waveforms were quantified and multicompartmental modeling (NEURON) was used to link the observed behavior with the underlying biophysical properties. The best-fit model included a non-uniform sodium channel distribution with decreasing conductance with distance from the soma, together with a non-uniform (increasing) A-type potassium conductance. AP amplitudes decline with distance in this model, but to a lesser extent than previously thought. We used this model to explore the mechanisms underlying two sets of published data involving high frequency trains of action potentials, and the local generation of sodium spikelets. ..."
186.  Excitation-contraction coupling/mitochondrial energetics (ECME) model (Cortassa et al. 2006)
"An intricate network of reactions is involved in matching energy supply with demand in the heart. This complexity arises because energy production both modulates and is modulated by the electrophysiological and contractile activity of the cardiac myocyte. Here, we present an integrated mathematical model of the cardiac cell that links excitation-contraction coupling with mitochondrial energy generation. The dynamics of the model are described by a system of 50 ordinary differential equations. The formulation explicitly incorporates cytoplasmic ATP-consuming processes associated with force generation and ion transport, as well as the creatine kinase reaction. Changes in the electrical and contractile activity of the myocyte are coupled to mitochondrial energetics through the ATP, Ca21, and Na1 concentrations in the myoplasmic and mitochondrial matrix compartments. ..."
187.  Excitatory and inhibitory interactions in populations of model neurons (Wilson and Cowan 1972)
Coupled nonlinear differential equations are derived for the dynamics of spatially localized populations containing both excitatory and inhibitory model neurons. Phase plane methods and numerical solutions are then used to investigate population responses to various types of stimuli. The results obtained show simple and multiple hysteresis phenomena and limit cycle activity. The latter is particularly interesting since the frequency of the limit cycle oscillation is found to be a monotonic function of stimulus intensity. Finally, it is proved that the existence of limit cycle dynamics in response to one class of stimuli implies the existence of multiple stable states and hysteresis in response to a different class of stimuli. The relation between these findings and a number of experiments is discussed.
188.  Excitatory and inhibitory population activity (Bittner et al 2017) (Litwin-Kumar & Doiron 2017)
"Many studies use population analysis approaches, such as dimensionality reduction, to characterize the activity of large groups of neurons. To date, these methods have treated each neuron equally, without taking into account whether neurons are excitatory or inhibitory. We studied population activity structure as a function of neuron type by applying factor analysis to spontaneous activity from spiking networks with balanced excitation and inhibition. Throughout the study, we characterized population activity structure by measuring its dimensionality and the percentage of overall activity variance that is shared among neurons. First, by sampling only excitatory or only inhibitory neurons, we found that the activity structures of these two populations in balanced networks are measurably different. We also found that the population activity structure is dependent on the ratio of excitatory to inhibitory neurons sampled. Finally we classified neurons from extracellular recordings in the primary visual cortex of anesthetized macaques as putative excitatory or inhibitory using waveform classification, and found similarities with the neuron type-specific population activity structure of a balanced network with excitatory clustering. These results imply that knowledge of neuron type is important, and allows for stronger statistical tests, when interpreting population activity structure."
189.  Excitatory synaptic interactions in pyramidal neuron dendrites (Behabadi et al. 2012)
" ... We hypothesized that if two excitatory pathways bias their synaptic projections towards proximal vs. distal ends of the basal branches, the very different local spike thresholds and attenuation factors for inputs near and far from the soma might provide the basis for a classical-contextual functional asymmetry. Supporting this possibility, we found both in compartmental models and electrophysiological recordings in brain slices that the responses of basal dendrites to spatially separated inputs are indeed strongly asymmetric. ..."
190.  Failure of Deep Brain Stimulation in a basal ganglia neuronal network model (Dovzhenok et al. 2013)
"… Recently, a lot of interest has been devoted to desynchronizing delayed feedback deep brain stimulation (DBS). ... This study explores the action of delayed feedback stimulation on partially synchronized oscillatory dynamics, similar to what one observes experimentally in parkinsonian patients. …" Implemented by Andrey Dovzhenok, to whom questions should be addressed.
191.  Fast global oscillations in networks of I&F neurons with low firing rates (Brunel and Hakim 1999)
Dynamics of a network of sparsely connected inhibitory current-based integrate-and-fire neurons. Individual neurons fire irregularly at low rate but the network is in an oscillatory global activity regime where neurons are weakly synchronized.
192.  Fast oscillations in inhibitory networks (Maex, De Schutter 2003)
We observed a new phenomenon of resonant synchronization in computer-simulated networks of inhibitory neurons in which the synaptic current has a delayed onset, reflecting finite spike propagation and synaptic transmission times. At the resonant level of network excitation, all neurons fire synchronously and rhythmically with a period approximately four times the mean delay of the onset of the inhibitory synaptic current. ... By varying the axonal delay of the inhibitory connections, networks with a realistic synaptic kinetics can be tuned to frequencies from 40 to >200 Hz. ... We conclude that the delay of the synaptic current is the primary parameter controlling the oscillation frequency of inhibitory networks and propose that delay-induced synchronization is a mechanism for fast brain rhythms that depend on intact inhibitory synaptic transmission.
193.  Fast-spiking cortical interneuron (Golomb et al. 2007)
Cortical fast-spiking (FS) interneurons display highly variable electrophysiological properties. We hypothesize that this variability emerges naturally if one assumes a continuous distribution of properties in a small set of active channels. We construct a minimal, single-compartment conductance-based model of FS cells that includes transient Na+, delayed-rectifier K+, and slowly inactivating d-type K+ conductances. The model may display delay to firing. Stuttering (elliptic bursting) and subthreshold oscillations may be observed for small Na+ window current.
194.  Febrile seizure-induced modifications to Ih (Chen et al 2001)
Modeling and experiments in the paper Chen K,Aradi I, Thom N,Eghbal-Ahmadi M, Baram TZ, and Soltesz I (2001) support the hypothesis that modified Ih currents strongly influence inhibitory inputs in CA1 cells and that the depolarizing shift in Ih activation plays a primary role in this process. Please see the paper for details. Some modeling details are available at http://www.ucihs.uci.edu/anatomy/soltesz/supp.htm Correspondance should be addressed to isoltesz@uci.edu (modeling was done by Ildiko Aradi, iaradi@uci.edu)
195.  Feedforward heteroassociative network with HH dynamics (Lytton 1998)
Using the original McCulloch-Pitts notion of simple on and off spike coding in lieu of rate coding, an Anderson-Kohonen artificial neural network (ANN) associative memory model was ported to a neuronal network with Hodgkin-Huxley dynamics.
196.  Firing neocortical layer V pyramidal neuron (Reetz et al. 2014; Stadler et al. 2014)
Neocortical Layer V model with firing behaviour adjusted to in vitro observations. The model was used to investigate the effects of IFN and PKC on the excitability of neurons (Stadler et al 2014, Reetz et al. 2014). The model contains new channel simulations for HCN1, HCN2 and the big calcium dependent potassium channel BK.
197.  Firing patterns in stuttering fast-spiking interneurons (Klaus et al. 2011)
This is a morphologically extended version of the fast-spiking interneuron by Golomb et al. (2007). The model captures the stuttering firing pattern and subthreshold oscillations in response to step current input as observed in many cortical and striatal fast-spiking cells.
198.  Fluctuating synaptic conductances recreate in-vivo-like activity (Destexhe et al 2001)
This model (and experiments) reported in Destexhe, Rudolh, Fellous, and Sejnowski (2001) support the hypothesis that many of the characteristics of cortical neurons in vivo can be explained by fast glutamatergic and GABAergic conductances varying stochastically. Some of these cortical neuron characteristics of fluctuating synaptic origin are a depolarized membrane potential, the presence of high-amplitude membrane potential fluctuations, a low input resistance and irregular spontaneous firing activity. In addition, the point-conductance model could simulate the enhancement of responsiveness due to background activity. For more information please contact Alain Destexhe. email: Destexhe@iaf.cnrs-gif.fr
199.  Formation of synfire chains (Jun and Jin 2007)
"Temporally precise sequences of neuronal spikes that span hundreds of milliseconds are observed in many brain areas, including songbird premotor nucleus, cat visual cortex, and primary motor cortex. Synfire chains—networks in which groups of neurons are connected via excitatory synapses into a unidirectional chain—are thought to underlie the generation of such sequences. It is unknown, however, how synfire chains can form in local neural circuits, especially for long chains. Here, we show through computer simulation that long synfire chains can develop through spike-time dependent synaptic plasticity and axon remodeling—the pruning of prolific weak connections that follows the emergence of a finite number of strong connections. ..."
200.  FS Striatal interneuron: K currents solve signal-to-noise problems (Kotaleski et al 2006)
... We show that a transient potassium (KA) current allows the Fast Spiking (FS) interneuron to strike a balance between sensitivity to correlated input and robustness to noise, thereby increasing its signal-to-noise ratio (SNR). First, a compartmental FS neuron model was created to match experimental data from striatal FS interneurons in cortex–striatum–substantia nigra organotypic cultures. Densities of sodium, delayed rectifier, and KA channels were optimized to replicate responses to somatic current injection. Spontaneous AMPA and GABA synaptic currents were adjusted to the experimentally measured amplitude, rise time, and interevent interval histograms. Second, two additional adjustments were required to emulate the remaining experimental observations. GABA channels were localized closer to the soma than AMPA channels to match the synaptic population reversal potential. Correlation among inputs was required to produce the observed firing rate during up-states. In this final model, KA channels were essential for suppressing down-state spikes while allowing reliable spike generation during up-states. ... Our results suggest that KA channels allow FS interneurons to operate without a decrease in SNR during conditions of increased dopamine, as occurs in response to reward or anticipated reward. See paper for more and details.
201.  Fully continuous Pinsky-Rinzel model for bifurcation analysis (Atherton et al. 2016)
The original, 2-compartment, CA3 cell, Pinsky-Rinzel model (Pinsky, Rinzel 1994) has several discontinuous functions that prevent the use of standard bifurcation analysis tools to study the model. Here we present a modified, fully continuous system that captures the behaviour of the original model, while permitting the use of available numerical continuation software to perform full-system bifurcation and fast-slow analysis in XPPAUT.
202.  Functional consequences of cortical circuit abnormalities on gamma in schizophrenia (Spencer 2009)
"Schizophrenia is characterized by cortical circuit abnormalities, which might be reflected in gamma-frequency (30–100 Hz) oscillations in the electroencephalogram. Here we used a computational model of cortical circuitry to examine the effects that neural circuit abnormalities might have on gamma generation and network excitability. The model network consisted of 1000 leaky integrateand- fi re neurons with realistic connectivity patterns and proportions of neuron types [pyramidal cells (PCs), regular-spiking inhibitory interneurons, and fast-spiking interneurons (FSIs)]. ... The results of this study suggest that a multimodal approach, combining non-invasive neurophysiological and structural measures, might be able to distinguish between different neural circuit abnormalities in schizophrenia patients. ..."
203.  Gamma and theta rythms in biophysical models of hippocampus circuits (Kopell et al. 2011)
" ... the main rhythms displayed by the hippocampus, the gamma (30–90 Hz) and theta (4–12 Hz) rhythms. We concentrate on modeling in vitro experiments, but with an eye toward possible in vivo implications. ... We use simpler biophysical models; all cells have a single compartment only, and the interneurons are restricted to two types: fast-spiking (FS) basket cells and oriens lacunosum-moleculare (O-LM) cells. ... , we aim not so much at reproducing dynamics in great detail, but at clarifying the essential mechanisms underlying the production of the rhythms and their interactions (Kopell, 2005). ..."
204.  Gamma genesis in the basolateral amygdala (Feng et al 2019)
Using in vitro and in vivo data we develop the first large-scale biophysically and anatomically realistic model of the basolateral amygdala nucleus (BL), which reproduces the dynamics of the in vivo local field potential (LFP). Significantly, it predicts that BL intrinsically generates the transient gamma oscillations observed in vivo. The model permitted exploration of the poorly understood synaptic mechanisms underlying gamma genesis in BL, and the model's ability to compute LFPs at arbitrary numbers of recording sites provided insights into the characteristics of the spatial properties of gamma bursts. Furthermore, we show how gamma synchronizes principal cells to overcome their low firing rates while simultaneously promoting competition, potentially impacting their afferent selectivity and efferent drive, and thus emotional behavior.
205.  Gamma oscillations in hippocampal interneuron networks (Bartos et al 2002)
To examine whether an interneuron network with fast inhibitory synapses can act as a gamma frequency oscillator, we developed an interneuron network model based on experimentally determined properties. In comparison to previous interneuron network models, our model was able to generate oscillatory activity with higher coherence over a broad range of frequencies (20-110 Hz). In this model, high coherence and flexibility in frequency control emerge from the combination of synaptic properties, network structure, and electrical coupling.
206.  Gamma oscillations in hippocampal interneuron networks (Wang, Buzsaki 1996)
The authors investigated the hypothesis that 20-80Hz neuronal (gamma) oscillations can emerge in sparsely connected network models of GABAergic fast-spiking interneurons. They explore model NN synchronization and compare their results to anatomical and electrophysiological data from hippocampal fast spiking interneurons.
207.  Gap junction coupled network of striatal fast spiking interneurons (Hjorth et al. 2009)
Gap junctions between striatal FS neurons has very weak ability to synchronise spiking. Input uncorrelated between neighbouring neurons is shunted, while correlated input is not.
208.  Gap-junction coupled network activity depends on coupled dendrites diameter (Gansert et al. 2007)
"... We have previously shown that the amplitude of electrical signals propagating across gap-junctionally coupled passive cables is maximized at a unique diameter. This suggests that threshold-dependent signals may propagate through gap junctions for a finite range of diameters around this optimal value. Here we examine the diameter dependence of action potential propagation across model networks of dendro-dendritically coupled neurons. The neurons in these models have passive soma and dendrites and an action potential-generating axon. We show that propagation of action potentials across gap junctions occurs only over a finite range of dendritic diameters and that propagation delay depends on this diameter. ...". See paper for more and details.
209.  Gating of steering signals through phasic modulation of reticulospinal neurons (Kozlov et al. 2014)
" ... We use the lamprey as a model for investigating the role of this phasic modulation of the reticulospinal activity, because the brainstem–spinal cord networks are known down to the cellular level in this phylogenetically oldest extant vertebrate. We describe how the phasic modulation of reticulospinal activity from the spinal CPG ensures reliable steering/turning commands without the need for a very precise timing of on- or offset, by using a biophysically detailed large-scale (19,600 model neurons and 646,800 synapses) computational model of the lamprey brainstem–spinal cord network. To verify that the simulated neural network can control body movements, including turning, the spinal activity is fed to a mechanical model of lamprey swimming. ..."
210.  Generating coherent patterns of activity from chaotic neural networks (Sussillo and Abbott 2009)
"Neural circuits display complex activity patterns both spontaneously and when responding to a stimulus or generating a motor output. How are these two forms of activity related? We develop a procedure called FORCE learning for modifying synaptic strengths either external to or within a model neural network to change chaotic spontaneous activity into a wide variety of desired activity patterns. ... Our results reproduce data on premovement activity in motor and premotor cortex, and suggest that synaptic plasticity may be a more rapid and powerful modulator of network activity than generally appreciated."
211.  Generating oscillatory bursts from a network of regular spiking neurons (Shao et al. 2009)
Avian nucleus isthmi pars parvocellularis (Ipc) neurons are reciprocally connected with the tectal layer 10 (L10) neurons and respond with oscillatory bursts to visual stimulation. To elucidate mechanisms of oscillatory bursting in this network of regularly spiking neurons, we investigated an experimentally constrained model of coupled leaky integrate-and-fire neurons with spike-rate adaptation. The model reproduces the observed Ipc oscillatory bursting in response to simulated visual stimulation.
212.  Global structure, robustness, and modulation of neuronal models (Goldman et al. 2001)
"The electrical characteristics of many neurons are remarkably robust in the face of changing internal and external conditions. At the same time, neurons can be highly sensitive to neuromodulators. We find correlates of this dual robustness and sensitivity in a global analysis of the structure of a conductance-based model neuron. ..."
213.  Glutamate-evoked Ca2+ oscillations in single astrocytes (De Pitta et al. 2009) (Manninen et al 2017)
We tested the reproducibility and comparability of four astrocyte models (Manninen, Havela, Linne, 2017). Model by De Pitta et al. (2009) was one of them. We implemented and ran the model by De Pitta et al. (2009) using Jupyter Notebook. Model code produces results of Figure 1 and Figures 3-5 in Manninen, Havela, Linne (2017).
214.  Glutamate-evoked Ca2+ oscillations in single astrocytes (Modified from Dupont et al. 2011)
We tested the reproducibility and comparability of four astrocyte models (Manninen, Havela, Linne, 2017). Model by Dupont et al. (2011) was one of them, but we had to modify the model to get more similar results as in the original publication. We implemented and ran the modified model using Jupyter Notebook. Model code produces results of Figure 1 and Figures 3-5 in Manninen, Havela, Linne (2017).
215.  GP Neuron, somatic and dendritic phase response curves (Schultheiss et al. 2011)
Phase response analysis of a GP neuron model showing type I PRCs for somatic inputs and type II PRCs for dendritic excitation. Analysis of intrinsic currents underlying type II dendritic PRCs.
216.  Grid cell oscillatory interference with noisy network oscillators (Zilli and Hasselmo 2010)
To examine whether an oscillatory interference model of grid cell activity could work if the oscillators were noisy neurons, we implemented these simulations. Here the oscillators are networks (either synaptically- or gap-junction--coupled) of one or more noisy neurons (either Izhikevich's simple model or a Hodgkin-Huxley--type biophysical model) which drive a postsynaptic cell (which may be integrate-and-fire, resonate-and-fire, or the simple model) which should fire spatially as a grid cell if the simulation is successful.
217.  Grid cell spatial firing models (Zilli 2012)
This package contains MATLAB implementations of most models (published from 2005 to 2011) of the hexagonal firing field arrangement of grid cells.
218.  Half-center oscillator database of leech heart interneuron model (Doloc-Mihu & Calabrese 2011)
We have created a database (HCO-db) of instances of a half-center oscillator computational model [Hill et al., 2001] for analyzing how neuronal parameters influence network activity. We systematically explored the parameter space of about 10.4 million simulated HCO instances and corresponding isolated neuron model simulations obtained by varying a set of selected parameters (maximal conductance of intrinsic and synaptic currents) in all combinations using a brute-force approach. We classified these HCO instances by their activity characteristics into identifiable groups. We built an efficient relational database table (HCO-db) with the resulting instances characteristics.
219.  High dimensional dynamics and low dimensional readouts in neural microcircuits (Haeusler et al 2006)
We investigate generic models for cortical microcircuits, i.e. recurrent circuits of integrate-and fire neurons with dynamic synapses. These complex dynamic systems subserve the amazing information processing capabilities of the cortex, but are at the present time very little understood. We analyze the transient dynamics of models for neural microcircuits from the point of view of one or two readout neurons that collapse the high dimensional transient dynamics of a neural circuit into a 1- or 2--dimensional output stream. See paper for more and details.
220.  High frequency oscillations in a hippocampal computational model (Stacey et al. 2009)
"... Using a physiological computer model of hippocampus, we investigate random synaptic activity (noise) as a potential initiator of HFOs (high-frequency oscillations). We explore parameters necessary to produce these oscillations and quantify the response using the tools of stochastic resonance (SR) and coherence resonance (CR). ... Our results show that, under normal coupling conditions, synaptic noise was able to produce gamma (30–100 Hz) frequency oscillations. Synaptic noise generated HFOs in the ripple range (100–200 Hz) when the network had parameters similar to pathological findings in epilepsy: increased gap junctions or recurrent synaptic connections, loss of inhibitory interneurons such as basket cells, and increased synaptic noise. ... We propose that increased synaptic noise and physiological coupling mechanisms are sufficient to generate gamma oscillations and that pathologic changes in noise and coupling similar to those in epilepsy can produce abnormal ripples."
221.  High frequency oscillations induced in three gap-junction coupled neurons (Tseng et al. 2008)
Here we showed experimentally that high frequency oscillations (up to 600 Hz) were easily induced in a purely gap-junction coupled network by simple two stimuli with very short interval. The root cause is that the second elicited spike suffered from slow propagation speed and failure to transmit through a low-conductance junction. Similiar results were also obtained in these simulation.
222.  High frequency stimulation of the Subthalamic Nucleus (Rubin and Terman 2004)
" ... Using a computational model, this paper considers the hypothesis that DBS works by replacing pathologically rhythmic basal ganglia output with tonic, high frequency firing. In our simulations of parkinsonian conditions, rhythmic inhibition from GPi to the thalamus compromises the ability of thalamocortical relay (TC) cells to respond to depolarizing inputs, such as sensorimotor signals. High frequency stimulation of STN regularizes GPi firing, and this restores TC responsiveness, despite the increased frequency and amplitude of GPi inhibition to thalamus that result. We provide a mathematical phase plane analysis of the mechanisms that determine TC relay capabilities in normal, parkinsonian, and DBS states in a reduced model. This analysis highlights the differences in deinactivation of the low-threshold calcium T -current that we observe in TC cells in these different conditions. ..."
223.  Hippocampal basket cell gap junction network dynamics (Saraga et al. 2006)
2 cell network of hippocampal basket cells connected by gap junctions. Paper explores how distal gap junctions and active dendrites can tune network dynamics.
224.  Hippocampal CA1 NN with spontaneous theta, gamma: full scale & network clamp (Bezaire et al 2016)
This model is a full-scale, biologically constrained rodent hippocampal CA1 network model that includes 9 cells types (pyramidal cells and 8 interneurons) with realistic proportions of each and realistic connectivity between the cells. In addition, the model receives realistic numbers of afferents from artificial cells representing hippocampal CA3 and entorhinal cortical layer III. The model is fully scaleable and parallelized so that it can be run at small scale on a personal computer or large scale on a supercomputer. The model network exhibits spontaneous theta and gamma rhythms without any rhythmic input. The model network can be perturbed in a variety of ways to better study the mechanisms of CA1 network dynamics. Also see online code at http://bitbucket.org/mbezaire/ca1 and further information at http://mariannebezaire.com/models/ca1
225.  Hippocampal CA3 network and circadian regulation (Stanley et al. 2013)
This model produces the hippocampal CA3 neural network model used in the paper below. It has two modes of operation, a default mode and a circadian mode. In the circadian mode, parameters are swept through a range of values. This model can be quite easily adapted to produce theta and gamma oscillations, as certain parameter sweeps will reveal (see Figures). BASH scripts interact with GENESIS 2.3 to implement parameter sweeps. The model contains four cell types derived from prior papers. CA3 pyramidal are derived from Traub et al (1991); Basket, stratum oriens (O-LM), and Medial Septal GABAergic (MSG) interneurons are taken from Hajos et al (2004).
226.  Hippocampus temporo-septal engram shift model (Lytton 1999)
Temporo-septal engram shift model of hippocampal memory. The model posits that memories gradually move along the hippocampus from a temporal encoding site to ever more septal sites from which they are recalled. We propose that the sense of time is encoded by the location of the engram along the temporo-septal axis.
227.  Hodgkin-Huxley model of persistent activity in PFC neurons (Winograd et al. 2008) (NEURON python)
The paper demonstrate a form of graded persistent activity activated by hyperpolarization. This phenomenon is modeled based on a slow calcium regulation of Ih, similar to that introduced earlier for thalamic neurons (see Destexhe et al., J Neurophysiol. 1996). The only difference is that the calcium signal is here provided by the high-threshold calcium current (instead of the low-threshold calcium current in thalamic neurons).
228.  Hodgkin-Huxley model of persistent activity in prefrontal cortex neurons (Winograd et al. 2008)
The paper demonstrate a form of graded persistent activity activated by hyperpolarization. This phenomenon is modeled based on a slow calcium regulation of Ih, similar to that introduced earlier for thalamic neurons (see Destexhe et al., J Neurophysiol. 1996). The only difference is that the calcium signal is here provided by the high-threshold calcium current (instead of the low-threshold calcium current in thalamic neurons).
229.  Hodgkin-Huxley simplifed 2D and 3D models (Lundstrom et al. 2009)
"Neuronal responses are often characterized by the firing rate as a function of the stimulus mean, or the f–I curve. We introduce a novel classification of neurons into Types A, B&#8722;, and B+ according to how f–I curves are modulated by input fluctuations. ..."
230.  Hodgkin–Huxley model with fractional gating (Teka et al. 2016)
We use fractional order derivatives to model the kinetic dynamics of the gate variables for the potassium and sodium conductances of the Hodgkin-Huxley model. Our results show that power-law dynamics of the different gate variables result in a wide range of action potential shapes and spiking patterns, even in the case where the model was stimulated with constant current. As a consequence, power-law behaving conductances result in an increase in the number of spiking patterns a neuron can generate and, we propose, expand the computational capacity of the neuron.
231.  Homosynaptic plasticity in the tail withdrawal circuit (TWC) of Aplysia (Baxter and Byrne 2006)
The tail-withdrawal circuit of Aplysia provides a useful model system for investigating synaptic dynamics. Sensory neurons within the circuit manifest several forms of synaptic plasticity. Here, we developed a model of the circuit and investigated the ways in which depression (DEP) and potentiation (POT) contributed to information processing. DEP limited the amount of motor neuron activity that could be elicited by the monosynaptic pathway alone. POT within the monosynaptic pathway did not compensate for DEP. There was, however, a synergistic interaction between POT and the polysynaptic pathway. This synergism extended the dynamic range of the network, and the interplay between DEP and POT made the circuit respond preferentially to long-duration, low-frequency inputs.
232.  Hopfield and Brody model (Hopfield, Brody 2000)
NEURON implementation of the Hopfield and Brody model from the papers: JJ Hopfield and CD Brody (2000) JJ Hopfield and CD Brody (2001). Instructions are provided in the below readme.txt file.
233.  Hopfield and Brody model (Hopfield, Brody 2000) (NEURON+python)
Demonstration of Hopfield-Brody snychronization using artificial cells in NEURON+python.
234.  Human Attentional Networks: A Connectionist Model (Wang and Fan 2007)
"... We describe a connectionist model of human attentional networks to explore the possible interplays among the networks from a computational perspective. This model is developed in the framework of leabra (local, error-driven, and associative, biologically realistic algorithm) and simultaneously involves these attentional networks connected in a biologically inspired way. ... We evaluate the model by simulating the empirical data collected on normal human subjects using the Attentional Network Test (ANT). The simulation results fit the experimental data well. In addition, we show that the same model, with a single parameter change that affects executive control, is able to simulate the empirical data collected from patients with schizophrenia. This model represents a plausible connectionist explanation for the functional structure and interaction of human attentional networks."
235.  Human seizures couple across spatial scales through travelling wave dynamics (Martinet et al 2017)
" ... We show that during seizure large-scale neural populations spanning centimetres of cortex coordinate with small neural groups spanning cortical columns, and provide evidence that rapidly propagating waves of activity underlie this increased inter-scale coupling. We develop a corresponding computational model to propose specific mechanisms—namely, the effects of an increased extracellular potassium concentration diffusing in space—that support the observed spatiotemporal dynamics. Understanding the multi-scale, spatiotemporal dynamics of human seizures—and connecting these dynamics to specific biological mechanisms—promises new insights to treat this devastating disease.
236.  Hyperconnectivity, slow synapses in PFC mental retardation and autism model (Testa-Silva et al 2011)
The subdirectory 'matlab' contains MATLAB scripts (The Mathworks, USA) that can be used to reproduce the panels of Figures 4-5. This directory contains files to reproduce sample computer simulations presented in the 2011 paper authored by Meredith, R., Testa-Silva, G., Loebel, A., Giugliano, M., de Kock, C.; Mansvelder, H. "Hyperconnectivity and slow synapses in prefrontal cortex of a model for mental retardation and autism". ABSTRACT "... We propose that these findings are tightly linked: using a network model, we show that slower synapses are essential to counterbalance hyperconnectivity in order to maintain a dynamic range of excitatory activity. However, the slow synaptic time constants induce decreased responsiveness to low frequency stimulation, which may explain deficits in integration and information processing in attentional neuronal networks in neurodevelopmental disorders."
237.  I&F recurrent networks with current- or conductance-based synapses (Cavallari et al. 2014)
Recurrent networks of two populations (excitatory and inhibitory) of randomly connected Leaky Integrate-and-Fire (LIF) neurons with either current- or conductance-based synapses from the paper S. Cavallari, S. Panzeri and A. Mazzoni (2014)
238.  Ih tunes oscillations in an In Silico CA3 model (Neymotin et al. 2013)
" ... We investigated oscillatory control using a multiscale computer model of hippocampal CA3, where each cell class (pyramidal, basket, and oriens-lacunosum moleculare cells), contained type-appropriate isoforms of Ih. Our model demonstrated that modulation of pyramidal and basket Ih allows tuning theta and gamma oscillation frequency and amplitude. Pyramidal Ih also controlled cross-frequency coupling (CFC) and allowed shifting gamma generation towards particular phases of the theta cycle, effected via Ih’s ability to set pyramidal excitability. ..."
239.  Impact of dendritic size and topology on pyramidal cell burst firing (van Elburg and van Ooyen 2010)
The code provided here was written to systematically investigate which of the physical parameters controlled by dendritic morphology underlies the differences in spiking behaviour observed in different realizations of the 'ping-pong'-model. Structurally varying dendritic topology and length in a simplified model allows us to separate out the physical parameters derived from morphology underlying burst firing. To perform the parameter scans we created a new NEURON tool the MultipleRunControl which can be used to easily set up a parameter scan and write the simulation results to file. Using this code we found that not input conductance but the arrival time of the return current, as measured provisionally by the average electrotonic path length, determines whether the pyramidal cell (with ping-pong model dynamics) will burst or fire single spikes.
240.  Inferior Olive, subthreshold oscillations (Torben-Nielsen, Segev, Yarom 2012)
The Inferior Olive is a brain structure in which neurons are solely connected to each other through gap-junctions. Its behavior is characterized by spontaneous subthreshold oscillation, frequency changes in the subthreshold oscillation, stable phase differences between neurons, and propagating waves of activity. Our model based on actual IO topology can reproduce these behaviors and provides a mechanistic explanation thereof.
241.  Influence of dendritic structure on neocortical neuron firing patterns (Mainen and Sejnowski 1996)
This package contains compartmental models of four reconstructed neocortical neurons (layer 3 Aspiny, layer 4 Stellate, layer 3 and layer 5 Pyramidal neurons) with active dendritic currents using NEURON. Running this simulation demonstrates that an entire spectrum of firing patterns can be reproduced in this set of model neurons which share a common distribution of ion channels and differ only in their dendritic geometry. The reference paper is: Z. F. Mainen and T. J. Sejnowski (1996) Influence of dendritic structure on firing pattern in model neocortical neurons. Nature 382: 363-366. See also http://www.cnl.salk.edu/~zach/methods.html and http://www.cnl.salk.edu/~zach/ More info in readme.txt file below made visible by clicking on the patdemo folder and then on the readme.txt file.
242.  Information-processing in lamina-specific cortical microcircuits (Haeusler and Maass 2006)
A major challenge for computational neuroscience is to understand the computational function of lamina-specific synaptic connection patterns in stereotypical cortical microcircuits.We approach this problem by studying ... the dynamical system defined by more realistic cortical microcircuit models as a whole and by investigating the influence that its laminar structure has on the transmission and fusion of information within this dynamical system. The circuit models that we examine consist of Hodgkin--Huxley neurons with dynamic synapses... We investigate to what extent this cortical microcircuit template supports the accumulation and fusion of information contained in generic spike inputs into layer 4 and layers 2/3 and how well it makes this information accessible to projection neurons in layers 2/3 and layer 5. ... We conclude that computer simulations of detailed lamina-specific cortical microcircuit models provide new insight into computational consequences of anatomical and physiological data. See paper for more and details.
243.  Infraslow intrinsic rhythmogenesis in a subset of AOB projection neurons (Gorin et al 2016)
We investigated patterns of spontaneous neuronal activity in mouse accessory olfactory bulb mitral cells, the direct neural link between vomeronasal sensory input and limbic output. Both in vitro and in vivo, we identify a subpopulation of mitral cells that exhibit slow stereotypical rhythmic discharge. In intrinsically rhythmogenic neurons, these periodic activity patterns are maintained in absence of fast synaptic drive. The physiological mechanism underlying mitral cell autorhythmicity involves cyclic activation of three interdependent ionic conductances: subthreshold persistent Na(+) current, R-type Ca(2+) current, and Ca(2+)-activated big conductance K(+) current. Together, the interplay of these distinct conductances triggers infraslow intrinsic oscillations with remarkable periodicity, a default output state likely to affect sensory processing in limbic circuits. The model reproduces the intrinsic firing in a reconstructed single AOB mitral cell with ion channels kinetics fitted to experimental measurements of their steady state and time course.
244.  Inhibition and glial-K+ interaction leads to diverse seizure transition modes (Ho & Truccolo 2016)
"How focal seizures initiate and evolve in human neocortex remains a fundamental problem in neuroscience. Here, we use biophysical neuronal network models of neocortical patches to study how the interaction between inhibition and extracellular potassium ([K+]o) dynamics may contribute to different types of focal seizures. Three main types of propagated focal seizures observed in recent intracortical microelectrode recordings in humans were modelled ..."
245.  Inhibitory control by an integral feedback signal in prefrontal cortex (Miller and Wang 2006)
The prefrontal cortex (PFC) is known to be critical for inhibitory control of behavior, but the underlying mechanisms are unclear. Here, we propose that inhibitory control can be instantiated by an integral signal derived from working memory, another key function of the PFC. Speci&#64257;cally, we assume that an integrator converts excitatory input into a graded mnemonic activity that provides an inhibitory signal (integral feedback control) to upstream afferent neurons. We demonstrate this scenario in a neuronal-network model for a temporal discrimination task... See paper for details and more.
246.  Interaction of leak and IMI conductance on the STG over broad temperature range (Stadele et al 2015)
The ZIP file contains a Hodgkin-Huxley based circuit model and the simulation environment MadSim used to study the interaction of leak and IMI on the gastric mill network of the crab (Cancer borealis) as represented in (C. Städele, S. Heigele and W. Stein, 2015) MadSim, the simulation environment used for this study, is freeware and included in the package.
247.  Interneuron Specific 3 Interneuron Model (Guet-McCreight et al, 2016)
In this paper we develop morphologically detailed multi-compartment models of Hippocampal CA1 interneuron specific 3 interneurons using cell current-clamp recordings and dendritic calcium imaging data. In doing so, we developed several variant models, as outlined in the associated README.html file.
248.  Inverse stochastic resonance of cerebellar Purkinje cell (Buchin et al. 2016)
This code shows the simulations of the adaptive exponential integrate-and-fire model (http://www.scholarpedia.org/article/Adaptive_exponential_integrate-and-fire_model) at different stimulus conditions. The parameters of the model were tuned to the Purkinje cell of cerebellum to reproduce the inhibiion of these cells by noisy current injections. Similar experimental protocols were also applied to the detailed biophysical model of Purkinje cells, de Shutter & Bower (1994) model. The repository also includes the XPPaut version of the model with the corresponding bifurcation analysis.
249.  Investigation of different targets in deep brain stimulation for Parkinson`s (Pirini et al. 2009)
"We investigated by a computational model of the basal ganglia the different network effects of deep brain stimulation (DBS) for Parkinson’s disease (PD) in different target sites in the subthalamic nucleus (STN), the globus pallidus pars interna (GPi), and the globus pallidus pars externa (GPe). A cellular-based model of the basal ganglia system (BGS), based on the model proposed by Rubin and Terman (J Comput Neurosci 16:211–235, 2004), was developed. ... Our results suggest that DBS in the STN could functionally restore the TC relay activity, while DBS in the GPe and in the GPi could functionally over-activate and inhibit it, respectively. Our results are consistent with the experimental and the clinical evidences on the network effects of DBS."
250.  Ion concentration dynamics as a mechanism for neuronal bursting (Barreto & Cressman 2011)
"We describe a simple conductance-based model neuron that includes intra and extracellular ion concentration dynamics and show that this model exhibits periodic bursting. The bursting arises as the fast-spiking behavior of the neuron is modulated by the slow oscillatory behavior in the ion concentration variables and vice versa. By separating these time scales and studying the bifurcation structure of the neuron, we catalog several qualitatively different bursting profiles that are strikingly similar to those seen in experimental preparations. Our work suggests that ion concentration dynamics may play an important role in modulating neuronal excitability in real biological systems."
251.  Ionic basis of alternans and Timothy Syndrome (Fox et al. 2002), (Zhu and Clancy 2007)
From Zhu and Clancy: "... Here we employ theoretical simulations to examine the effects of a Timothy Syndrome (TS) mutation in the L-type Ca2+ channel on cardiac dynamics over multiple scales, from a gene mutation to protein, cell, tissue, and finally the ECG, to connect a defective Ca2+ channel to arrhythmia susceptibility. ..."
252.  Ionic current model of a Hypoglossal Motoneuron (Purvis & Butera 2005)
"We have developed a single-compartment, electrophysiological, hypoglossal motoneuron (HM) model based primarily on experimental data from neonatal rat HMs. The model is able to reproduce the fine features of the HM action potential: the fast afterhyperpolarization, the afterdepolarization, and the medium-duration afterhyperpolarization (mAHP). The model also reproduces the repetitive firing properties seen in neonatal HMs and replicates the neuron’s response to pharmacological experiments. The model was used to study the role of specific ionic currents in HM firing and how variations in the densities of these currents may account for age dependent changes in excitability seen in HMs. ..."
253.  Ionic mechanisms of bursting in CA3 pyramidal neurons (Xu and Clancy 2008)
"... We present a single-compartment model of a CA3 hippocampal pyramidal neuron based on recent experimental data. We then use the model to determine the roles of primary depolarizing currents in burst generation. The single compartment model incorporates accurate representations of sodium (Na+) channels (NaV1.1) and T-type calcium (Ca2+) channel subtypes (CaV3.1, CaV3.2, and CaV3.3). Our simulations predict the importance of Na+ and T-type Ca2+ channels in hippocampal pyramidal cell bursting and reveal the distinct contribution of each subtype to burst morphology. We also performed fastslow analysis in a reduced comparable model, which shows that our model burst is generated as a result of the interaction of two slow variables, the T-type Ca2+ channel activation gate and the Ca2+-dependent potassium (K+) channel activation gate. The model reproduces a range of experimentally observed phenomena including afterdepolarizing potentials, spike widening at the end of the burst, and rebound. Finally, we use the model to simulate the effects of two epilepsy-linked mutations: R1648H in NaV1.1 and C456S in CaV3.2, both of which result in increased cellular excitability."
254.  Irregular oscillations produced by cyclic recurrent inhibition (Friesen, Friesen 1994)
Model of recurrent cyclic inhibition as described on p.119 of Friesen and Friesen (1994), which was slightly modified from Szekely's model (1965) of a network for producing alternating limb movements.
255.  Irregular spiking in NMDA-driven prefrontal cortex neurons (Durstewitz and Gabriel 2006)
Slow N-Methyl-D-aspartic acid (NMDA) synaptic currents are assumed to strongly contribute to the persistently elevated firing rates observed in prefrontal cortex (PFC) during working memory. During persistent activity, spiking of many neurons is highly irregular. ... The highest interspike-interval (ISI) variability occurred in a transition regime where the subthreshold membrane potential distribution shifts from mono- to bimodality, ... Predictability within irregular ISI series was significantly higher than expected from a noise-driven linear process, indicating that it might best be described through complex (potentially chaotic) nonlinear deterministic processes. Accordingly, the phenomena observed in vitro could be reproduced in purely deterministic biophysical model neurons. High spiking irregularity in these models emerged within a chaotic, close-to-bifurcation regime characterized by a shift of the membrane potential distribution from mono- to bimodality and by similar ISI return maps as observed in vitro. ... NMDA-induced irregular dynamics may have important implications for computational processes during working memory and neural coding.
256.  Ketamine disrupts theta modulation of gamma in a computer model of hippocampus (Neymotin et al 2011)
"Abnormalities in oscillations have been suggested to play a role in schizophrenia. We studied theta-modulated gamma oscillations in a computer model of hippocampal CA3 in vivo with and without simulated application of ketamine, an NMDA receptor antagonist and psychotomimetic. Networks of 1200 multi-compartment neurons (pyramidal, basket and oriens-lacunosum moleculare, OLM, cells) generated theta and gamma oscillations from intrinsic network dynamics: basket cells primarily generated gamma and amplified theta, while OLM cells strongly contributed to theta. ..."
257.  KInNeSS : a modular framework for computational neuroscience (Versace et al. 2008)
The xml files provided here implement a network of excitatory and inhibitory spiking neurons, governed by either Hodgkin-Huxley or quadratic integrate-and-fire dynamical equations. The code is used to demonstrate the capabilities of the KInNeSS software package for simulation of networks of spiking neurons. The simulation protocol used here is meant to facilitate the comparison of KInNeSS with other simulators reviewed in <a href="http://dx.doi.org/10.1007/s10827-007-0038-6">Brette et al. (2007)</a>. See the associated paper "Versace et al. (2008) KInNeSS : a modular framework for computational neuroscience." for an extensive description of KInNeSS .
258.  Knox implementation of Destexhe 1998 spike and wave oscillation model (Knox et al 2018)
" ...The aim of this study was to use an established thalamocortical computer model to determine how T-type calcium channels work in concert with cortical excitability to contribute to pathogenesis and treatment response in CAE. METHODS: The model is comprised of cortical pyramidal, cortical inhibitory, thalamocortical relay, and thalamic reticular single-compartment neurons, implemented with Hodgkin-Huxley model ion channels and connected by AMPA, GABAA , and GABAB synapses. Network behavior was simulated for different combinations of T-type calcium channel conductance, inactivation time, steady state activation/inactivation shift, and cortical GABAA conductance. RESULTS: Decreasing cortical GABAA conductance and increasing T-type calcium channel conductance converted spindle to spike and wave oscillations; smaller changes were required if both were changed in concert. In contrast, left shift of steady state voltage activation/inactivation did not lead to spike and wave oscillations, whereas right shift reduced network propensity for oscillations of any type...."
259.  KV1 channel governs cerebellar output to thalamus (Ovsepian et al. 2013)
The output of the cerebellum to the motor axis of the central nervous system is orchestrated mainly by synaptic inputs and intrinsic pacemaker activity of deep cerebellar nuclear (DCN) projection neurons. Herein, we demonstrate that the soma of these cells is enriched with KV1 channels produced by mandatory multi-merization of KV1.1, 1.2 alpha andKV beta2 subunits. Being constitutively active, the K+ current (IKV1) mediated by these channels stabilizes the rate and regulates the temporal precision of self-sustained firing of these neurons. ... Through the use of multi-compartmental modelling and ... the physiological significance of the described functions for processing and communication of information from the lateral DCN to thalamic relay nuclei is established.
260.  Laminar analysis of excitatory circuits in vibrissal motor and sensory cortex (Hooks et al. 2011)
"... We mapped local excitatory pathways in each area (primary motor cortex (vM1), primary somatosensory cortex (vS1; barrel cortex), and secondary somatosensory cortex (S2)) across all cortical layers using glutamate uncaging and laser scanning photostimulation. We analyzed these maps to derive laminar connectivity matrices describing the average strengths of pathways between individual neurons in different layers and between entire cortical layers. ..."
261.  Laminar connectivity matrix simulation (Weiler et al 2008)
A routine that simulates the flow of activity within and across laminar levels in the local pyramidal neuron network, based on a connectivity matrix (W) measured by laser scanning photostimulation in mouse somatic motor cortex, and a very simple neural network simulation.
262.  Large cortex model with map-based neurons (Rulkov et al 2004)
We develop a new computationally efficient approach for the analysis of complex large-scale neurobiological networks. Its key element is the use of a new phenomenological model of a neuron capable of replicating important spike pattern characteristics and designed in the form of a system of difference equations (a map). ... Interconnected with synaptic currents these model neurons demonstrated responses very similar to those found with Hodgkin-Huxley models and in experiments. We illustrate the efficacy of this approach in simulations of one- and two-dimensional cortical network models consisting of regular spiking neurons and fast spiking interneurons to model sleep and activated states of the thalamocortical system. See paper for more.
263.  Large scale model of the olfactory bulb (Yu et al., 2013)
The readme file currently contains links to the results for all the 72 odors investigated in the paper, and the movie showing the network activity during learning of odor k3-3 (an aliphatic ketone).
264.  Large-scale laminar model of macaque cortex (Mejias et al 2016)
This code reproduces the large-scale cortical model with laminar structure presented in Mejias et al., Science Advances 2016. The model includes different scales (intra-laminar, inter-laminar, inter-areal and large-scale) across macaque neocortex and reproduces experimentally observed dynamics of gamma and alpha/beta oscillations across these scales. It makes use of real anatomical data from the macaque cortex. Some parts of the code require external packages or data (see readme file for details).
265.  Late emergence of the whisker direction selectivity map in rat barrel cortex (Kremer et al. 2011)
"... We discovered that the emergence of a direction map in rat barrel cortex occurs long after all known critical periods in the somatosensory system. This map is remarkably specific, taking a pinwheel-like form centered near the barrel center and aligned to the barrel cortex somatotopy. We suggest that this map may arise from intracortical mechanisms and demonstrate by simulation that the combination of spike-timing-dependent plasticity at synapses between layer 4 and layer 2/3 and realistic pad stimulation is sufficient to produce such a map. ..."
266.  Lateral dendrodenditic inhibition in the Olfactory Bulb (David et al. 2008)
Mitral cells, the principal output neurons of the olfactory bulb, receive direct synaptic activation from primary sensory neurons. Shunting inhibitory inputs delivered by granule cell interneurons onto mitral cell lateral dendrites are believed to influence spike timing and underlie coordinated field potential oscillations. Lateral dendritic shunt conductances delayed spiking to a degree dependent on both their electrotonic distance and phase of onset. Recurrent inhibition significantly narrowed the distribution of mitral cell spike times, illustrating a tendency towards coordinated synchronous activity. This result suggests an essential role for early mechanisms of temporal coordination in olfaction. The model was adapted from Davison et al, 2003, but include additional noise mechanisms, long lateral dendrite, and specific synaptic point processes.
267.  Layer V PFC pyramidal neuron used to study persistent activity (Sidiropoulou & Poirazi 2012)
"... Here, we use a compartmental modeling approach to search for discriminatory features in the properties of incoming stimuli to a PFC pyramidal neuron and/or its response that signal which of these stimuli will result in persistent activity emergence. Furthermore, we use our modeling approach to study cell-type specific differences in persistent activity properties, via implementing a regular spiking (RS) and an intrinsic bursting (IB) model neuron. ... Collectively, our results pinpoint to specific features of the neuronal response to a given stimulus that code for its ability to induce persistent activity and predict differential roles of RS and IB neurons in persistent activity expression. "
268.  Leech Heart (HE) Motor Neuron conductances contributions to NN activity (Lamb & Calabrese 2013)
"... To explore the relationship between conductances, and in particular how they influence the activity of motor neurons in the well characterized leech heartbeat system, we developed a new multi-compartmental Hodgkin-Huxley style leech heart motor neuron model. To do so, we evolved a population of model instances, which differed in the density of specific conductances, capable of achieving specific output activity targets given an associated input pattern. ... We found that the strengths of many conductances, including those with differing dynamics, had strong partial correlations and that these relationships appeared to be linked by their influence on heart motor neuron activity. Conductances that had positive correlations opposed one another and had the opposite effects on activity metrics when perturbed whereas conductances that had negative correlations could compensate for one another and had similar effects on activity metrics. "
269.  Leech heart interneuron network model (Hill et al 2001, 2002)
We have created a computational model of the timing network that paces the heartbeat of the medicinal leech, Hirudo medicinalis. In the intact nerve cord, segmental oscillators are mutually entrained to the same cycle period. Although experiments have shown that the segmental oscillators are coupled by inhibitory coordinating interneurons, the underlying mechanisms of intersegmental coordination have not yet been elucidated. To help understand this coordination, we have created a simple computational model with two variants: symmetric and asymmetric. See references for more details. Biologically realistic network models with two, six, and eight cells and a tutorial are available at the links to Calabrese's web site below.
270.  Leech Mechanosensory Neurons: Synaptic Facilitation by Reflected APs (Baccus 1998)
This model by Stephen Baccus explores the phenomena of action potential (AP) propagation at branch boints in axons. APs are sometimes transmitted down the efferent processes and sometimes are reflected back to the axon of AP origin or neither. See the paper for details. The model zip file contains a readme.txt which list introductory steps to follow to run the simulation. Stephen Baccus's email address: baccus@fas.harvard.edu
271.  LFP signature of monosynaptic thalamocortical connection (Hagen et al 2017)
"A resurgence has taken place in recent years in the use of the extracellularly recorded local field potential (LFP) to investigate neural network activity. To probe monosynaptic thalamic activation of cortical postsynaptic target cells, so called spike-trigger-averaged LFP (stLFP) signatures have been measured. In these experiments, the cortical LFP is measured by multielectrodes covering several cortical lamina and averaged on spontaneous spikes of thalamocortical (TC) cells. Using a well established forward-modeling scheme, we investigated the biophysical origin of this stLFP signature with simultaneous synaptic activation of cortical layer-4 neurons, mimicking the effect of a single afferent spike from a single TC neuron. ..."
272.  LGMD with 3D morphology and active dendrites (Dewell & Gabbiani 2018)
This is a model of the locust LGMD looming sensitive neuron from Dewell & Gabbiani 2018. The morphology was constructed based on 2-photon imaging, and active conductances throughout the neuron were based on sharp electrode recordings in vivo.
273.  Linking dynamics of the inhibitory network to the input structure (Komarov & Bazhenov 2016)
Code to model 10 all-to-all coupled inhibitory neurons.
274.  Lobster STG pyloric network model with calcium sensor (Gunay & Prinz 2010) (Prinz et al. 2004)
This pyloric network model simulator is a C/C++ program that saves 384 different calcium sensor values that are candidates for activity sensors (Gunay and Prinz, 2010). The simulator was used to scan all of the 20 million pyloric network models that were previously collected in a database (Prinz et al, 2004).
275.  Long time windows from theta modulated inhib. in entorhinal–hippo. loop (Cutsuridis & Poirazi 2015)
"A recent experimental study (Mizuseki et al., 2009) has shown that the temporal delays between population activities in successive entorhinal and hippocampal anatomical stages are longer (about 70–80 ms) than expected from axon conduction velocities and passive synaptic integration of feed-forward excitatory inputs. We investigate via computer simulations the mechanisms that give rise to such long temporal delays in the hippocampus structures. ... The model shows that the experimentally reported long temporal delays in the DG, CA3 and CA1 hippocampal regions are due to theta modulated somatic and axonic inhibition..."
276.  Loss of phase-locking in non-weakly coupled inhib. networks of type-I neurons (Oh and Matveev 2009)
... Here we examine the loss of synchrony caused by an increase in inhibitory coupling in networks of type-I Morris–Lecar model oscillators, which is characterized by a period-doubling cascade and leads to mode-locked states with alternation in the firing order of the two cells, as reported recently by Maran and Canavier (J Comput Nerosci, 2008) for a network of Wang-Buzsáki model neurons. Although alternating-order firing has been previously reported as a near-synchronous state, we show that the stable phase difference between the spikes of the two Morris–Lecar cells can constitute as much as 70% of the unperturbed oscillation period. Further, we examine the generality of this phenomenon for a class of type-I oscillators that are close to their excitation thresholds, and provide an intuitive geometric description of such “leap-frog” dynamics. ..."
277.  Low dose of dopamine may stimulate prolactin secretion by increasing K currents (Tabak et al. 2006)
".. We considered the fast K+ currents flowing through large-conductance BK channels and through A-type channels. We developed a minimal lactotroph model to investigate the effects of these two currents. Both IBK and IA could transform the electrical pattern of activity from spiking to bursting, but through distinct mechanisms. IBK always increased the intracellular Ca2+ concentration, while IA could either increase or decrease it. Thus, the stimulatory effects of DA could be mediated by a fast K+ conductance which converts tonically spiking cells to bursters. In addition, the study illustrates that a heterogeneous distribution of fast K+ conductances could cause heterogeneous lactotroph firing patterns."
278.  Low Threshold Calcium Currents in TC cells (Destexhe et al 1998)
In Destexhe, Neubig, Ulrich, and Huguenard (1998) experiments and models examine low threshold calcium current's (IT, or T-current) distribution in thalamocortical (TC) cells. Multicompartmental modeling supports the hypothesis that IT currents have a density at least several fold higher in the dendrites than the soma. The IT current contributes significantly to rebound bursts and is thought to have important network behavior consequences. See the paper for details. See also http://cns.iaf.cnrs-gif.fr Correspondance may be addressed to Alain Destexhe: Destexhe@iaf.cnrs-gif.fr
279.  Low Threshold Calcium Currents in TC cells (Destexhe et al 1998) (Brian)
R Brette's implementation in Brian 2 of Destexhe et al 1998's model. The author's original code is also available from ModelDB with accession number 279 (yes, was one of the first models in ModelDB)!
280.  Mathematical model for windup (Aguiar et al. 2010)
"Windup is characterized as a frequency-dependent increase in the number of evoked action potentials in dorsal horn neurons in response to electrical stimulation of afferent C-fibers. ... The approach presented here relies on mathematical and computational analysis to study the mechanism(s) underlying windup. From experimentally obtained windup profiles, we extract the time scale of the facilitation mechanisms that may support the characteristics of windup. Guided by these values and using simulations of a biologically realistic compartmental model of a wide dynamic range (WDR) neuron, we are able to assess the contribution of each mechanism for the generation of action potentials windup. ..."
281.  MDD: the role of glutamate dysfunction on Cingulo-Frontal NN dynamics (Ramirez-Mahaluf et al 2017)
" ...Currently, no mechanistic framework describes how network dynamics, glutamate, and serotonin interact to explain MDD symptoms and treatments. Here, we built a biophysical computational model of 2 areas (vACC and dlPFC) that can switch between emotional and cognitive processing. (Major Depression Disease) MDD networks were simulated by slowing glutamate decay in vACC and demonstrated sustained vACC activation. ..."
282.  Mean-field systems and small scale neural networks (Ferguson et al. 2015)
We explore adaptation induced bursting as a mechanism for theta oscillations in hippocampal area CA1. To do this, we have developed a mean-field system for a network of fitted Izhikevich neurons with sparse coupling and heterogeneity. The code contained here runs the mean-field system pointwise or on a two-parameter mesh, in addition to networks of neurons that are smaller then those considered in the paper. The file README.pdf contains instructions on use. Note that the following file (peakfinder): http://www.mathworks.com/matlabcentral/fileexchange/25500-peakfinder-x0--sel--thresh--extrema--includeendpoints--interpolate- is required to compute burst frequencies in the mean-field system and must be downloaded and placed in the same root folder as MFSIMULATOR.mat
283.  MEC layer II stellate cell: Synaptic mechanisms of grid cells (Schmidt-Hieber & Hausser 2013)
This study investigates the cellular mechanisms of grid field generation in Medial Entorhinal Cortex (MEC) layer II stellate cells.
284.  Mechanisms of fast rhythmic bursting in a layer 2/3 cortical neuron (Traub et al 2003)
This simulation is based on the reference paper listed below. This port was made by Roger D Traub and Maciej T Lazarewicz (mlazarew at seas.upenn.edu) Thanks to Ashlen P Reid for help with porting a morphology of the cell.
285.  Mechanisms of very fast oscillations in axon networks coupled by gap junctions (Munro, Borgers 2010)
Axons connected by gap junctions can produce very fast oscillations (VFOs, > 80 Hz) when stimulated randomly at a low rate. The models here explore the mechanisms of VFOs that can be seen in an axonal plexus, (Munro & Borgers, 2009): a large network model of an axonal plexus, small network models of axons connected by gap junctions, and an implementation of the model underlying figure 12 in Traub et al. (1999) . The large network model consists of 3,072 5-compartment axons connected in a random network. The 5-compartment axons are the 5 axonal compartments from the CA3 pyramidal cell model in Traub et al. (1994) with a fixed somatic voltage. The random network has the same parameters as the random network in Traub et al. (1999), and axons are stimulated randomly via a Poisson process with a rate of 2/s/axon. The small network models simulate waves propagating through small networks of axons connected by gap junctions to study how local connectivity affects the refractory period.
286.  Mechanisms underlying different onset patterns of focal seizures (Wang Y et al 2017)
"Focal seizures are episodes of pathological brain activity that appear to arise from a localised area of the brain. The onset patterns of focal seizure activity have been studied intensively, and they have largely been distinguished into two types { low amplitude fast oscillations (LAF), or high amplitude spikes (HAS). Here we explore whether these two patterns arise from fundamentally different mechanisms. Here, we use a previously established computational model of neocortical tissue, and validate it as an adequate model using clinical recordings of focal seizures. We then reproduce the two onset patterns in their most defining properties and investigate the possible mechanisms underlying the different focal seizure onset patterns in the model. ..."
287.  Mechanisms underlying subunit independence in pyramidal neuron dendrites (Behabadi and Mel 2014)
"...Using a detailed compartmental model of a layer 5 pyramidal neuron, and an improved method for quantifying subunit independence that incorporates a more accurate model of dendritic integration, we first established that the output of each dendrite can be almost perfectly predicted by the intensity and spatial configuration of its own synaptic inputs, and is nearly invariant to the rate of bAP-mediated 'cross-talk' from other dendrites over a 100-fold range..."
288.  Medial reticular formation of the brainstem: anatomy and dynamics (Humphries et al. 2006, 2007)
A set of models to study the medial reticular formation (mRF) of the brainstem. We developed a collection of algorithms to derive the adult-state wiring of the model: one set a stochastic model; the other set mimicking the developmental process. We found that the anatomical models had small-world properties, irrespective of the choice of algorithm; and that the cluster-like organisation of the mRF may have arisen to minimise wiring costs. (The model code includes options to be run as dynamic models; papers examining these dynamics are included in the .zip file).
289.  Medial vestibular neuron models (Quadroni and Knopfel 1994)
The structure and the parameters of the model cells were chosen to reproduce the responses of type A and type B MVNns as described in electrophysiological recordings. The emergence of oscillatory firing under these two specific experimental conditions is consistent with electrophysiological recordings not used during construction of the model. We, therefore, suggest that these models have a high predictive value.
290.  Microsaccades and synchrony coding in the retina (Masquelier et al. 2016)
We show that microsaccades (MS) enable efficient synchrony-based coding among the primate retinal ganglion cells (RGC). We find that each MS causes certain RGCs to fire synchronously, namely those whose receptive fields contain contrast edges after the MS. The emitted synchronous spike volley thus rapidly transmits the most salient edges of the stimulus. We demonstrate that the readout could be done rapidly by simple coincidence-detector neurons, and that the required connectivity could emerge spontaneously with spike timing-dependent plasticity.
291.  Midbrain dopamine neuron: firing patterns (Canavier 1999)
Sodium dynamics drives the generation of slow oscillations postulated to underly NMDA-evoked bursting activity.
292.  Mitral cell activity gating by respiration and inhibition in an olfactory bulb NN (Short et al 2016)
To explore interactions between respiration, inhibition, and olfaction, experiments using light to active channel rhodopsin in sensory neurons expressing Olfactory Marker Protein were performed in mice and modeled in silico. This archive contains NEURON models that were run on parallel computers to explore the interactions between varying strengths of respiratory activity and olfactory sensory neuron input and the roles of periglomerular, granule, and external tufted cells in shaping mitral cell responses.
293.  Mixed mode oscillations as a mechanism for pseudo-plateau bursting (Vo et al. 2010)
"We combine bifurcation analysis with the theory of canard-induced mixed mode oscillations to investigate the dynamics of a novel form of bursting. This bursting oscillation, which arises from a model of the electrical activity of a pituitary cell, is characterized by small impulses or spikes riding on top of an elevated voltage plateau. ..."
294.  Model for concentration invariant odor coding based on primacy hypothesis (Wilson et al 2017)
"... Here we propose that, in olfaction, a small and relatively stable set comprised of the earliest activated receptors forms a code for concentration-invariant odor identity. One prediction of this “primacy coding” scheme is that decisions based on odor identity can be made solely using early odor-evoked neural activity. Using an optogenetic masking paradigm, we define the sensory integration time necessary for odor identification and demonstrate that animals can use information occurring <100ms after inhalation onset to identify odors. ... We propose a computational model demonstrating how such a code can be read by neural circuits of the olfactory system."
295.  Model for K-ATP mediated bursting in mSNc DA neurons (Knowlton et al 2018)
"Burst firing in medial substantia nigra dopamine (mSN DA) neurons has been selectively linked to novelty-induced exploration behavior in mice. Burst firing in mSN DA neurons, in contrast to lateral SN DA neurons, requires functional ATP-sensitive potassium channels (K-ATP) both in vitro and in vivo. However, the precise role of K-ATP channels in promoting burst firing is un-known. We show experimentally that L-type calcium channel activity in mSN DA neurons en-hances open probability of K-ATP channels. We then generated a mathematical model to study the role of Ca2+ dynamics driving K-ATP channel function in mSN DA neurons during bursting. ..."
296.  Model of arrhythmias in a cardiac cells network (Casaleggio et al. 2014)
" ... Here we explore the possible processes leading to the occasional onset and termination of the (usually) non-fatal arrhythmias widely observed in the heart. Using a computational model of a two-dimensional network of cardiac cells, we tested the hypothesis that an ischemia alters the properties of the gap junctions inside the ischemic area. ... In conclusion, our model strongly supports the hypothesis that non-fatal arrhythmias can develop from post-ischemic alteration of the electrical connectivity in a relatively small area of the cardiac cell network, and suggests experimentally testable predictions on their possible treatments."
297.  Model of calcium oscillations in olfactory cilia (Reidl et al. 2006)
Simulation of experiments on olfactory receptor neurons (ORNs). Focussing on the negative feedback that calcium (through calmodulin) has on its own influx through CNG channels, this model is able to reproduce both calcium oscillations as well as adaptation behaviour as seen in experiments done with ORNs.
298.  Model of long range transmission of gamma oscillation (Murray 2007)
"... A minimal mathematical model was developed for a preliminary study of long-range neural transmission of gamma oscillation from the CA3 to the entorhinal cortex via the CAI region of the hippocampus, a subset within a larger complex set of pathways. A module was created for each local population of neurons with common intrinsic properties and connectivity to simplify the connection process and make the model more flexible. Three modules were created using MATLAB Simulink® and tested to confirm that they transmit gamma through the system. The model also revealed that a portion of the signal from CAI to the entorhinal cortex may be lost in transmission under certain conditions."
299.  Model of neural responses to amplitude-modulated tones (Nelson and Carney 2004)
"A phenomenological model with time-varying excitation and inhibition was developed to study possible neural mechanisms underlying changes in the representation of temporal envelopes along the auditory pathway. A modified version of an existing auditory-nerve model (Zhang et al., J. Acoust. Soc. Am. 109, 648–670 (2001) was used to provide inputs to higher hypothetical processing centers. Model responses were compared directly to published physiological data at three levels: the auditory nerve, ventral cochlear nucleus, and inferior colliculus. ..."
300.  Model of repetitive firing in Grueneberg ganglion olfactory neurons (Liu et al., 2012)
This model is constructed based on properties of Na+ and K+ currents observed in whole-cell patch clamp recordings of mouse Grueneberg ganglion neurons in acute slices. Two distinct Na+ conductances representing the TTX-sensitive and TTX-resistant currents and one delayed rectifier K+ currrent are included. By modulating the maximal conductances of Na+ currents, one can reproduce the regular, phasic, and sporadic patterns of repetitive firing found in the patch clamp experiments.
301.  Model of SK current`s influence on precision in Globus Pallidus Neurons (Deister et al. 2009)
" ... In numerical simulations, the availability of both Na+ and A-type K+ channels during autonomous firing were reduced when SK channels were removed, and a nearly equal reduction in Na+ and K+ subthreshold-activated ion channel availability produced a large decrease in the neuron's slope conductance near threshold. This change made the neuron more sensitive to intrinsically generated noise. In vivo, this change would also enhance the sensitivity of GP (Globus Pallidus) neurons to small synaptic inputs."
302.  Model of the cerebellar granular network (Sudhakar et al 2017)
"The granular layer, which mainly consists of granule and Golgi cells, is the first stage of the cerebellar cortex and processes spatiotemporal information transmitted by mossy fiber inputs with a wide variety of firing patterns. To study its dynamics at multiple time scales in response to inputs approximating real spatiotemporal patterns, we constructed a large-scale 3D network model of the granular layer. ..."
303.  Model of the hippocampus over the sleep-wake cycle using Hodgkin-Huxley neurons (Aussel et al 2018)
" ...we propose a computational model of the hippocampal formation based on a realistic topology and synaptic connectivity, and we analyze the effect of different changes on the network, namely the variation of synaptic conductances, the variations of the CAN channel conductance and the variation of inputs. By using a detailed simulation of intracerebral recordings, we show that this is able to reproduce both the theta-nested gamma oscillations that are seen in awake brains and the sharp-wave ripple complexes measured during slow-wave sleep. The results of our simulations support the idea that the functional connectivity of the hippocampus, modulated by the sleep-wake variations in Acetylcholine concentration, is a key factor in controlling its rhythms."
304.  Model of the Xenopus tadpole swimming spinal network (Roberts et al. 2014)
This is a NEURON-python and MATLAB simulation code for generating anatomical or probabilistic connectivity and simulating the neuronal dynamics of the neuronal network controlling swimming in Xenopus tadpoles. For more details about this model, see Ferrario et al, 2018, eLife and Roberts et al, 2014, J of Neurosci
305.  Modeling interactions in Aplysia neuron R15 (Yu et al 2004)
"The biophysical properties of neuron R15 in Aplysia endow it with the ability to express multiple modes of oscillatory electrical activity, such as beating and bursting. Previous modeling studies examined the ways in which membrane conductances contribute to the electrical activity of R15 and the ways in which extrinsic modulatory inputs alter the membrane conductances by biochemical cascades and influence the electrical activity. The goals of the present study were to examine the ways in which electrical activity influences the biochemical cascades and what dynamical properties emerge from the ongoing interactions between electrical activity and these cascades." See paper for more and details.
306.  Modeling the effects of dopamine on network synchronization (Komek et al. 2012)
Dopamine modulates cortical circuit activity in part through its actions on GABAergic interneurons, including increasing the excitability of fast-spiking interneurons. Though such effects have been demonstrated in single cells, there are no studies that examine how such mechanisms may lead to the effects of dopamine at a neural network level. In this study, we investigated the effects of dopamine on synchronization in two simulated neural networks; one biophysical model composed of Wang-Buzsaki neurons and a reduced model with theta neurons. In both models, we show that parametrically varying the levels of dopamine, modeled through the changes in the excitability of interneurons, reveals an inverted-U shaped relationship, with low gamma band power at both low and high dopamine levels and optimal synchronization at intermediate levels. Moreover, such a relationship holds when the external input is both tonic and periodic at gamma band range. Together, our results indicate that dopamine can modulate cortical gamma band synchrony in an inverted-U fashion and that the physiologic effects of dopamine on single fast-spiking interneurons can give rise to such non-monotonic effects at the network level.
307.  Modelling enteric neuron populations and muscle fed-state motor patterns (Chambers et al. 2011)
"After a meal, the gastrointestinal tract exhibits a set of behaviours known as the fed state. ... Segmentation manifests as rhythmic local constrictions that do not propagate along the intestine. ... We investigated the enteric circuits that regulate segmentation focusing on a central feature of the ENS: a recurrent excitatory network of intrinsic sensory neurons (ISNs) which are characterized by prolonged after-hyperpolarizing potentials (AHPs) following their action potentials. ..."
308.  Modelling reduced excitability in aged CA1 neurons as a Ca-dependent process (Markaki et al. 2005)
"We use a multi-compartmental model of a CA1 pyramidal cell to study changes in hippocampal excitability that result from aging-induced alterations in calcium-dependent membrane mechanisms. The model incorporates N- and L-type calcium channels which are respectively coupled to fast and slow afterhyperpolarization potassium channels. Model parameters are calibrated using physiological data. Computer simulations reproduce the decreased excitability of aged CA1 cells, which results from increased internal calcium accumulation, subsequently larger postburst slow afterhyperpolarization, and enhanced spike frequency adaptation. We find that aging-induced alterations in CA1 excitability can be modelled with simple coupling mechanisms that selectively link specific types of calcium channels to specific calcium-dependent potassium channels."
309.  Models for cortical UP-DOWN states in a bistable inhibitory-stabilized network (Jercog et al 2017)
In the idling brain, neuronal circuits transition between periods of sustained firing (UP state) and quiescence (DOWN state), a pattern the mechanisms of which remain unclear. We analyzed spontaneous cortical population activity from anesthetized rats and found that UP and DOWN durations were highly variable and that population rates showed no significant decay during UP periods. We built a network rate model with excitatory (E) and inhibitory (I) populations exhibiting a novel bistable regime between a quiescent and an inhibition-stabilized state of arbitrarily low rate, where fluctuations triggered state transitions. In addition, we implemented these mechanisms in a more biophysically realistic spiking network, where DOWN-to-UP transitions are caused by synchronous high-amplitude events impinging onto the network.
310.  Modular grid cell responses as a basis for hippocampal remapping (Monaco and Abbott 2011)
"Hippocampal place fields, the local regions of activity recorded from place cells in exploring rodents, can undergo large changes in relative location during remapping. This process would appear to require some form of modulated global input. Grid-cell responses recorded from layer II of medial entorhinal cortex in rats have been observed to realign concurrently with hippocampal remapping, making them a candidate input source. However, this realignment occurs coherently across colocalized ensembles of grid cells (Fyhn et al., 2007). The hypothesized entorhinal contribution to remapping depends on whether this coherence extends to all grid cells, which is currently unknown. We study whether dividing grid cells into small numbers of independently realigning modules can both account for this localized coherence and allow for hippocampal remapping. ..."
311.  Modulation of septo-hippocampal theta activity by GABAA receptors (Hajos et al. 2004)
Theta frequency oscillation of the septo-hippocampal system has been considered as a prominent activity associated with cognitive function and affective processes. ... In the present experiments we applied a combination of computational and physiological techniques to explore the functional role of GABAA receptors in theta oscillation. ... In parallel to these experimental observations, a computational model has been constructed by implementing a septal GABA neuron model with a CA1 hippocampal model containing three types of neurons (including oriens and basket interneurons and pyramidal cells; latter modeled by multicompartmental techniques; for detailed model description with network parameters see online addendum: http://geza.kzoo.edu/theta). This connectivity made the network capable of simulating the responses of the septo-hippocampal circuitry to the modulation of GABAA transmission, and the presently described computational model proved suitable to reveal several aspects of pharmacological modulation of GABAA receptors. In addition, computational findings indicated different roles of distinctively located GABAA receptors in theta generation.
312.  Morris-Lecar model of the barnacle giant muscle fiber (Morris, Lecar 1981)
... This paper presents an analysis of the possible modes of behavior available to a system of two noninactivating conductance mechanisms, and indicates a good correspondence to the types of behavior exhibited by barnacle fiber. The differential equations of a simple equivalent circuit for the fiber are dealt with by means of some of the mathematical techniques of nonlinear mechanics. General features of the system are (a) a propensity to produce damped or sustained oscillations over a rather broad parameter range, and (b) considerable latitude in the shape of the oscillatory potentials. It is concluded that for cells subject to changeable parameters (either from cell to cell or with time during cellular activity), a system dominated by two noninactivating conductances can exhibit varied oscillatory and bistable behavior. See paper for details.
313.  Motion Clouds: Synthesis of random textures for motion perception (Leon et al. 2012)
We describe a framework to generate random texture movies with controlled information content. In particular, these stimuli can be made closer to naturalistic textures compared to usual stimuli such as gratings and random-dot kinetograms. We simplified the definition to parametrically define these "Motion Clouds" around the most prevalent feature axis (mean and bandwith): direction, spatial frequency, orientation.
314.  Motor cortex microcircuit simulation based on brain activity mapping (Chadderdon et al. 2014)
"... We developed a computational model based primarily on a unified set of brain activity mapping studies of mouse M1. The simulation consisted of 775 spiking neurons of 10 cell types with detailed population-to-population connectivity. Static analysis of connectivity with graph-theoretic tools revealed that the corticostriatal population showed strong centrality, suggesting that would provide a network hub. ... By demonstrating the effectiveness of combined static and dynamic analysis, our results show how static brain maps can be related to the results of brain activity mapping."
315.  Multi-timescale adaptive threshold model (Kobayashi et al 2009)
" ... In this study, we devised a simple, fast computational model that can be tailored to any cortical neuron not only for reproducing but also for predicting a variety of spike responses to greatly fluctuating currents. The key features of this model are a multi-timescale adaptive threshold predictor and a nonresetting leaky integrator. This model is capable of reproducing a rich variety of neuronal spike responses, including regular spiking, intrinsic bursting, fast spiking, and chattering, by adjusting only three adaptive threshold parameters. ..."
316.  Multi-timescale adaptive threshold model (Kobayashi et al 2009) (NEURON)
" ... In this study, we devised a simple, fast computational model that can be tailored to any cortical neuron not only for reproducing but also for predicting a variety of spike responses to greatly fluctuating currents. The key features of this model are a multi-timescale adaptive threshold predictor and a nonresetting leaky integrator. This model is capable of reproducing a rich variety of neuronal spike responses, including regular spiking, intrinsic bursting, fast spiking, and chattering, by adjusting only three adaptive threshold parameters. ..."
317.  Multiple dynamical modes of thalamic relay neurons (Wang XJ 1994)
The (Wang 1994) papers model was replicated in python by (Detorakis 2016). "The model is conductance-based and takes advantage of the interplay between a T-type calcium current and a non-specific cation sag current and thus, it is able to generate spindle and delta rhythms." The model also generates intermittent phase locking, non periodic firing, bursts, and tonic spike patterns.
318.  Multiple modes of a conditional neural oscillator (Epstein, Marder 1990)
We present a model for a conditional bursting neuron consisting of five conductances: Hodgkin-Huxley type time- and voltage-dependent Na+ and K+ conductances, a calcium activated voltage-dependent K+ conductance, a calcium-inhibited time- and voltage-dependent Ca++ conductance, and a leakage Cl- conductance. Different bursting and silent modes and transitions between them are analyzed in the model and compared to bursting modes in experiment. See the paper for details.
319.  Multiple modes of inner hair cell stimulation (Mountain, Cody 1999)
This model simulates the membrane potential of an inner hair cell for a sinusoidal stimulus to the hair bundle. It uses a 2-state Boltzmann model for the tension-gated conductance in the stereocilia and a linear model for the basolateral membrane. This model is based on the IHC model used in Mountain and Cody (1999).
320.  Multiscale model of olfactory receptor neuron in mouse (Dougherty 2009)
Collection of XPP (.ode) files simulating the signal transduction (slow) and action potential (fast) currents in the olfactory receptor neuron of mouse. Collection contains model configured for dual odorant pulse delivery and model configured for prolonged odorant delivery. For those interested more in transduction processes, each whole cell recording model comes with a counter part file configured to show just the slow transduction current for ease of use and convenience. These transduction-only models typically run faster than the full multi-scale models but do not demonstrate action potentials.
321.  Multiscale modeling of epileptic seizures (Naze et al. 2015)
" ... In the context of epilepsy, the functional properties of the network at the source of a seizure are disrupted by a possibly large set of factors at the cellular and molecular levels. It is therefore needed to sacrifice some biological accuracy to model seizure dynamics in favor of macroscopic realizations. Here, we present a neuronal network model that convenes both neuronal and network representations with the goal to describe brain dynamics involved in the development of epilepsy. We compare our modeling results with animal in vivo recordings to validate our approach in the context of seizures. ..."
322.  Multisensory integration in the superior colliculus: a neural network model (Ursino et al. 2009)
" ... The model includes three distinct neural areas: two unimodal areas (auditory and visual) are devoted to a topological representation of external stimuli, and communicate via synaptic connections with a third downstream area (in the SC) responsible for multisensory integration. The present simulations show that the model, with a single set of parameters, can mimic various responses to different combinations of external stimuli including the inverse effectiveness, both in terms of multisensory enhancement and contrast, the existence of within- and cross-modality suppression between spatially disparate stimuli, a reduction of network settling time in response to cross-modal stimuli compared with individual stimuli. ..."
323.  Multistability of clustered states in a globally inhibitory network (Chandrasekaran et al. 2009)
"We study a network of m identical excitatory cells projecting excitatory synaptic connections onto a single inhibitory interneuron, which is reciprocally coupled to all excitatory cells through inhibitory synapses possessing short-term synaptic depression. We find that such a network with global inhibition possesses multiple stable activity patterns with distinct periods, characterized by the clustering of the excitatory cells into synchronized sub-populations. We prove the existence and stability of n-cluster solutions in a m-cell network. ... Implications for temporal coding and memory storage are discussed."
324.  Multitarget pharmacology for Dystonia in M1 (Neymotin et al 2016)
" ... We developed a multiscale model of primary motor cortex, ranging from molecular, up to cellular, and network levels, containing 1715 compartmental model neurons with multiple ion channels and intracellular molecular dynamics. We wired the model based on electrophysiological data obtained from mouse motor cortex circuit mapping experiments. We used the model to reproduce patterns of heightened activity seen in dystonia by applying independent random variations in parameters to identify pathological parameter sets. ..."
325.  MyFirstNEURON (Houweling, Sejnowski 1997)
MyFirstNEURON is a NEURON demo by Arthur Houweling and Terry Sejnowski. Perform experiments from the book 'Electrophysiology of the Neuron, A Companion to Shepherd's Neurobiology, An Interactive Tutorial' by John Huguenard & David McCormick, Oxford University Press 1997, or design your own one or two cell simulation.
326.  NAcc medium spiny neuron: effects of cannabinoid withdrawal (Spiga et al. 2010)
Cannabinoid withdrawal produces a hypofunction of dopaminergic neurons targeting medium spiny neurons (MSN) of the forebrain. Administration of a CB1 receptor antagonist to control rats provoked structural abnormalities, reminiscent of those observed in withdrawal conditions and support the regulatory role of cannabinoids in neurogenesis, axonal growth and synaptogenesis. Experimental observations were incorporated into a realistic computational model which predicts a strong reduction in the excitability of morphologically-altered MSN, yielding a significant reduction in action potential output. These paper provided direct morphological evidence for functional abnormalities associated with cannabinoid dependence at the level of dopaminergic neurons and their post synaptic counterpart, supporting a hypodopaminergic state as a distinctive feature of the “addicted brain”.
327.  Neocort. pyramidal cells subthreshold somatic voltage controls spike propagation (Munro Kopell 2012)
There is suggestive evidence that pyramidal cell axons in neocortex may be coupled by gap junctions into an ``axonal plexus" capable of generating Very Fast Oscillations (VFOs) with frequencies exceeding 80 Hz. It is not obvious, however, how a pyramidal cell in such a network could control its output when action potentials are free to propagate from the axons of other pyramidal cells into its own axon. We address this problem by means of simulations based on 3D reconstructions of pyramidal cells from rat somatosensory cortex. We show that somatic depolarization enables propagation via gap junctions into the initial segment and main axon, while somatic hyperpolarization disables it. We show further that somatic voltage cannot effectively control action potential propagation through gap junctions on minor collaterals; action potentials may therefore propagate freely from such collaterals regardless of somatic voltage. In previous work, VFOs are all but abolished during the hyperpolarization phase of slow-oscillations induced by anesthesia in vivo. This finding constrains the density of gap junctions on collaterals in our model and suggests that axonal sprouting due to cortical lesions may result in abnormally high gap junction density on collaterals, leading in turn to excessive VFO activity and hence to epilepsy via kindling.
328.  Neocortical Layer I: I-A and I-K (Zhou, Hablitz 1996)
NEURON mod files for the I-A and I-K currents from the paper: Zhou FM, Hablitz JJ. Layer I neurons of the rat neocortex. II. Voltage-dependent outward currents. J Neurophysiol 1996 76:668-82.
329.  Network bursts in cultured NN result from different adaptive mechanisms (Masquelier & Deco 2013)
It is now well established that cultured neuron networks are spontaneously active, and tend to synchronize. Synchronous events typically involve the whole network, and have thus been termed “network spikes” (NS). Using experimental recordings and numerical simulations, we show here that the inter-NS interval statistics are complex, and allow inferring the neural mechanisms at work, in particular the adaptive ones, and estimating a number of parameters to which we cannot access experimentally.
330.  Network model of the granular layer of the cerebellar cortex (Maex, De Schutter 1998)
We computed the steady-state activity of a large-scale model of the granular layer of the rat cerebellum. Within a few tens of milliseconds after the start of random mossy fiber input, the populations of Golgi and granule cells became entrained in a single synchronous oscillation, the basic frequency of which ranged from 10 to 40 Hz depending on the average rate of firing in the mossy fiber population. ... The synchronous, rhythmic firing pattern was robust over a broad range of biologically realistic parameter values and to parameter randomization. Three conditions, however, made the oscillations more transient and could desynchronize the entire network in the end: a very low mossy fiber activity, a very dominant excitation of Golgi cells through mossy fiber synapses (rather than through parallel fiber synapses), and a tonic activation of granule cell GABAA receptors (with an almost complete absence of synaptically induced inhibitory postsynaptic currents). The model predicts that, under conditions of strong mossy fiber input to the cerebellum, Golgi cells do not only control the strength of parallel fiber activity but also the timing of the individual spikes. Provided that their parallel fiber synapses constitute an important source of excitation, Golgi cells fire rhythmically and synchronized with granule cells over large distances along the parallel fiber axis. See paper for more and details.
331.  Network model with neocortical architecture (Anderson et al 2007,2012; Azhar et al 2012)
Architecturally realistic neocortical model using seven classes of excitatory and inhibitory single compartment Hodgkin-Huxley cells. This is an addendum to ModelDB Accession # 98902, Studies of stimulus parameters for seizure disruption (Anderson et al. 2007). Wiring is adapted from the minicolumn hypothesis and incorporates visual and neocortical wiring data. Simulation demonstrates spontaneous bursting onset and cessation. This activity can be induced by random fluctuations in the surrounding background input.
332.  Network recruitment to coherent oscillations in a hippocampal model (Stacey et al. 2011)
"... Here we demonstrate, via a detailed computational model, a mechanism whereby physiological noise and coupling initiate oscillations and then recruit neighboring tissue, in a manner well described by a combination of Stochastic Resonance and Coherence Resonance. We develop a novel statistical method to quantify recruitment using several measures of network synchrony. This measurement demonstrates that oscillations spread via preexisting network connections such as interneuronal connections, recurrent synapses, and gap junctions, provided that neighboring cells also receive sufficient inputs in the form of random synaptic noise. ..."
333.  Network topologies for producing limited sustained activation (Kaiser and Hilgetag 2010)
Uses networks of cellular automata to test hypotheses about network topologies that can produce limited, sustained activity. Inspired by empirically-based ideas about neocortical architecture, but conceived and implemented at a level of abstraction that is not closely linked to empirical observations.
334.  Networks of spiking neurons: a review of tools and strategies (Brette et al. 2007)
This package provides a series of codes that simulate networks of spiking neurons (excitatory and inhibitory, integrate-and-fire or Hodgkin-Huxley type, current-based or conductance-based synapses; some of them are event-based). The same networks are implemented in different simulators (NEURON, GENESIS, NEST, NCS, CSIM, XPP, SPLIT, MVAspike; there is also a couple of implementations in SciLab and C++). The codes included in this package are benchmark simulations; see the associated review paper (Brette et al. 2007). The main goal is to provide a series of benchmark simulations of networks of spiking neurons, and demonstrate how these are implemented in the different simulators overviewed in the paper. See also details in the enclosed file Appendix2.pdf, which describes these different benchmarks. Some of these benchmarks were based on the Vogels-Abbott model (Vogels TP and Abbott LF 2005).
335.  Neural mass model of spindle generation in the isolated thalamus (Schellenberger Costa et al. 2016)
The model generates different oscillatory patterns in the thalamus, including delta and spindle band oscillations.
336.  Neural mass model of the neocortex under sleep regulation (Costa et al 2016)
This model generates typical human EEG patterns of sleep stages N2/N3 as well as wakefulness and REM. It further contains a sleep regulatory component, that lets the model transition between those stages independently
337.  Neural mass model of the sleeping cortex (Weigenand et al 2014)
Generates typical EEG data of sleeping Humans for sleep stages N2/N3 as well as wakefulness
338.  Neural mass model of the sleeping thalamocortical system (Schellenberger Costa et al 2016)
This paper generates typical human EEG data of sleep stages N2/N3 as well as wakefulness and REM sleep.
339.  Neural model of frog ventilatory rhythmogenesis (Horcholle-Bossavit and Quenet 2009)
"In the adult frog respiratory system, periods of rhythmic movements of the buccal floor are interspersed by lung ventilation episodes. The ventilatory activity results from the interaction of two hypothesized oscillators in the brainstem. Here, we model these oscillators with two coupled neural networks, whose co-activation results in the emergence of new dynamics. .. The biological interest of this formal model is illustrated by the persistence of the relevant dynamical features when perturbations are introduced in the model, i.e. dynamic noises and architecture modifications. The implementation of the networks with clock-driven continuous time neurones provides simulations with physiological time scales."
340.  Neural modeling of an internal clock (Yamazaki and Tanaka 2008)
"We studied a simple random recurrent inhibitory network. Despite its simplicity, the dynamics was so rich that activity patterns of neurons evolved with time without recurrence due to random recurrent connections among neurons. The sequence of activity patterns was generated by the trigger of an external signal, and the generation was stable against noise.... Therefore, a time passage from the trigger of an external signal could be represented by the sequence of activity patterns, suggesting that this model could work as an internal clock. ..."
341.  Neural transformations on spike timing information (Tripp and Eliasmith 2007)
" ... Here we employ computational methods to show that an ensemble of neurons firing at a constant mean rate can induce arbitrarily chosen temporal current patterns in postsynaptic cells. ..."
342.  Neurogenesis in the olfactory bulb controlled by top-down input (Adams et al 2018)
This code implements a model for adult neurogenesis of granule cells in the olfactory system. The granule cells receive sensory input via the mitral cells and top-down input from a cortical area. That cortical area also receives olfactory input from the mitral cells as well as contextual input. This plasticity leads to a network structure consisting of bidirectional connections between bulbar and cortical odor representations. The top-down input enhances stimulus discrimination based on contextual input.
343.  Neuronal population models of intracerebral EEG (Wendling et al. 2005)
"... In this study, the authors relate electrophysiologic patterns typically observed during the transition from interictal to ictal activity in human mesial temporal lobe epilepsy (MTLE) to mechanisms (at a neuronal population level) involved in seizure generation through a computational model of EEG activity. Intracerebral EEG signals recorded from hippocampus in five patients with MTLE during four periods (during interictal activity, just before seizure onset, during seizure onset, and during ictal activity) were used to identify the three main parameters of a model of hippocampus EEG activity (related to excitation, slow dendritic inhibition and fast somatic inhibition). ... . Results demonstrated that the model generates very realistic signals for automatically identified parameters. They also showed that the transition from interictal to ictal activity cannot be simply explained by an increase in excitation and a decrease in inhibition but rather by time-varying ensemble interactions between pyramidal cells and local interneurons projecting to either their dendritic or perisomatic region (with slow and fast GABAA kinetics). Particularly, during preonset activity, an increasing dendritic GABAergic inhibition compensates a gradually increasing excitation up to a brutal drop at seizure onset when faster oscillations (beta and low gamma band, 15 to 40 Hz) are observed. ... These findings obtained from model identification in human temporal lobe epilepsy are in agreement with some results obtained experimentally, either on animal models of epilepsy or on the human epileptic tissue."
344.  Nigral dopaminergic neurons: effects of ethanol on Ih (Migliore et al. 2008)
We use a realistic computational model of dopaminergic neurons in vivo to suggest that ethanol, through its effects on Ih, modifies the temporal structure of the spiking activity. The model predicts that the dopamine level may increase much more during bursting than pacemaking activity, especially in those brain regions with a slow dopamine clearance rate. The results suggest that a selective pharmacological remedy could thus be devised against the rewarding effects of ethanol that are postulated to mediate alcohol abuse and addiction, targeting the specific HCN genes expressed in dopaminergic neurons.
345.  NMDA spikes in basal dendrites of L5 pyramidal neurons (Polsky et al. 2009)
"... In apical dendrites of neocortical pyramidal neurons, calcium spikes are known to contribute to burst generation, but a comparable understanding of basal dendritic mechanisms is lacking. Here we show that NMDA spikes in basal dendrites mediate both detection and generation of bursts through a postsynaptic mechanism. High-frequency inputs to basal dendrites markedly facilitated NMDA spike initiation compared with low-frequency activation or single inputs. ..."
346.  Nodes of Ranvier with left-shifted Nav channels (Boucher et al. 2012)
The two programs CLSRanvier.f and propagation.f simulate the excitability of a myelinated axon with injured nodes of Ranvier. The injury is simulated as the Coupled Left Shift (CLS) of the activation(V) and inactivation(V) (availability) of a fraction of Nav channels.
347.  Nodose sensory neuron (Schild et al. 1994, Schild and Kunze 1997)
This is a simulink implementation of the model described in Schild et al. 1994, and Schild and Kunze 1997 papers on Nodose sensory neurons. These papers describe the sensitivity these models have to their parameters and the match of the models to experimental data.
348.  Normal ripples, abnormal ripples, and fast ripples in a hippocampal model (Fink et al. 2015)
"...We use a computational model of hippocampus to investigate possible network mechanisms underpinning normal ripples, pathological ripples, and fast ripples. Our results unify several prior findings regarding HFO mechanisms, and also make several new predictions regarding abnormal HFOs. We show that HFOs are generic, emergent phenomena whose characteristics reflect a wide range of connectivity and network input. Although produced by different mechanisms, both normal and abnormal HFOs generate similar ripple frequencies, underscoring that peak frequency is unable to distinguish the two. Abnormal ripples are generic phenomena that arise when input to pyramidal cells overcomes network inhibition, resulting in high-frequency, uncoordinated firing. In addition, fast ripples transiently and sporadically arise from the precise conditions that produce abnormal ripples. Lastly, we show that such abnormal conditions do not require any specific network structure to produce coherent HFOs, as even completely asynchronous activity is capable of producing abnormal ripples and fast ripples in this manner. These results provide a generic, network-based explanation for the link between pathological ripples and fast ripples, and a unifying description for the entire spectrum from normal ripples to pathological fast ripples."
349.  Norns - Neural Network Studio (Visser & Van Gils 2014)
The Norns - Neural Network Studio is a software package for designing, simulation and analyzing networks of spiking neurons. It consists of three parts: 1. "Urd": a Matlab frontend with high-level functions for quickly defining networks 2. "Verdandi": an optimized C++ simulation environment which runs the simulation defined by Urd 3. "Skuld": an advanced Matlab graphical user interface (GUI) for visual inspection of simulated data.
350.  Novel Na current with slow de-inactivation (Tsutsui, Oka 2002)
The authors found a novel Na current in teleost thalamic nuclei was well described by the m^3 h Hodgkin-Huxley model. The kinetic parameters derived from their experiments (see the reference for details) revealed that the h gate had a large time constant (~100ms at -80 to -50mV). This explains the thalamic neurons long refractory period and the gradual recovery of AP amplitude as the inter spike interval grows.
351.  O-LM interneuron model (Lawrence et al. 2006)
Exploring the kinetics and distribution of the muscarinic potassium channel, IM, in 2 O-LM interneuron morphologies. Modulation of the ion channel by drugs such as XE991 (antagonist) and retigabine (agonist) are simulated in the models to examine the role of IM in spiking properties.
352.  Olfactory bulb mitral and granule cell column formation (Migliore et al. 2007)
In the olfactory bulb, the processing units for odor discrimination are believed to involve dendrodendritic synaptic interactions between mitral and granule cells. There is increasing anatomical evidence that these cells are organized in columns, and that the columns processing a given odor are arranged in widely distributed arrays. Experimental evidence is lacking on the underlying learning mechanisms for how these columns and arrays are formed. We have used a simplified realistic circuit model to test the hypothesis that distributed connectivity can self-organize through an activity-dependent dendrodendritic synaptic mechanism. The results point to action potentials propagating in the mitral cell lateral dendrites as playing a critical role in this mechanism, and suggest a novel and robust learning mechanism for the development of distributed processing units in a cortical structure.
353.  Olfactory bulb mitral and granule cell: dendrodendritic microcircuits (Migliore and Shepherd 2008)
This model shows how backpropagating action potentials in the long lateral dendrites of mitral cells, together with granule cell actions on mitral cells within narrow columns forming glomerular units, can provide a mechanism to activate strong local inhibition between arbitrarily distant mitral cells. The simulations predict a new role for the dendrodendritic synapses in the multicolumnar organization of the granule cells.
354.  Olfactory bulb mitral cell gap junction NN model: burst firing and synchrony (O`Connor et al. 2012)
In a network of 6 mitral cells connected by gap junction in the apical dendrite tuft, continuous current injections of 0.06 nA are injected into 20 locations in the apical tufts of two of the mitral cells. The current injections into one of the cells starts 10 ms after the other to generate asynchronous firing in the cells (Migliore et al. 2005 protocol). Firing of the cells is asynchronous for the first 120 ms. However after the burst firing phase is completed the firing in all cells becomes synchronous.
355.  Olfactory bulb mitral cell: synchronization by gap junctions (Migliore et al 2005)
In a realistic model of two electrically connected mitral cells, the paper shows that the somatically-measured experimental properties of Gap Junctions (GJs) may correspond to a variety of different local coupling strengths and dendritic distributions of GJs in the tuft. The model suggests that the propagation of the GJ-induced local tuft depolarization is a major mechanim for intraglomerular synchronization of mitral cells.
356.  Olfactory Bulb mitral-granule network generates beta oscillations (Osinski & Kay 2016)
This model of the dendrodendritic mitral-granule synaptic network generates gamma and beta oscillations as a function of the granule cell excitability, which is represented by the granule cell resting membrane potential.
357.  Olfactory Bulb Network (Davison et al 2003)
A biologically-detailed model of the mammalian olfactory bulb, incorporating the mitral and granule cells and the dendrodendritic synapses between them. The results of simulation experiments with electrical stimulation agree closely in most details with published experimental data. The model predicts that the time course of dendrodendritic inhibition is dependent on the network connectivity as well as on the intrinsic parameters of the synapses. In response to simulated odor stimulation, strongly activated mitral cells tend to suppress neighboring cells, the mitral cells readily synchronize their firing, and increasing the stimulus intensity increases the degree of synchronization. For more details, see the reference below.
358.  Olfactory bulb network model of gamma oscillations (Bathellier et al. 2006; Lagier et al. 2007)
This model implements a network of 100 mitral cells connected with asynchronous inhibitory "synapses" that is meant to reproduce the GABAergic transmission of ensembles of connected granule cells. For appropriate parameters of this special synapse the model generates gamma oscillations with properties very similar to what is observed in olfactory bulb slices (See Bathellier et al. 2006, Lagier et al. 2007). Mitral cells are modeled as single compartment neurons with a small number of different voltage gated channels. Parameters were tuned to reproduce the fast subthreshold oscillation of the membrane potential observed experimentally (see Desmaisons et al. 1999).
359.  Olfactory bulb network: neurogenetic restructuring and odor decorrelation (Chow et al. 2012)
Adult neurogenesis in the olfactory bulb has been shown experimentally to contribute to perceptual learning. Using a computational network model we show that fundamental aspects of the adult neurogenesis observed in the olfactory bulb -- the persistent addition of new inhibitory granule cells to the network, their activity-dependent survival, and the reciprocal character of their synapses with the principal mitral cells -- are sufficient to restructure the network and to alter its encoding of odor stimuli adaptively so as to reduce the correlations between the bulbar representations of similar stimuli. The model captures the experimentally observed role of neurogenesis in perceptual learning and the enhanced response of young granule cells to novel stimuli. Moreover, it makes specific predictions for the type of odor enrichment that should be effective in enhancing the ability of animals to discriminate similar odor mixtures. NSF grant DMS-0719944.
360.  Olfactory Mitral Cell (Bhalla, Bower 1993)
This is a conversion to NEURON of the mitral cell model described in Bhalla and Bower (1993). The original model was written in GENESIS and is available by joining BABEL, the GENESIS users' group here http://www.genesis-sim.org/GENESIS/babel.html
361.  Olfactory receptor neuron model (Dougherty et al 2005)
Demonstration of ORN model by Dougherty, Wright and Yew (2005) PNAS 102: 10415-10420. This program, dwy_pnas_demo2, simulates the transduction current response of a single olfactory receptor neuron being stimulated by an odorant plume. The program is interactive in that a user can tweak parameter values and stimulus conditions. Also, users can save a configuration in a mat-file or export all aspects to a directory of text files. These text files can be read by other programs. There is also an import facility for importing text files from a directory that allows the user to specify their own data, pulses and parameters.
362.  Optimal spatiotemporal spike pattern detection by STDP (Masquelier 2017)
We simulate a LIF neuron equipped with STDP. A pattern repeats in its inputs. The LIF progressively becomes selective to the repeating pattern, in an optimal manner.
363.  Orientation selectivity in inhibition-dominated recurrent networks (Sadeh and Rotter, 2015)
Emergence of contrast-invariant orientation selectivity in large-scale networks of excitatory and inhibitory neurons using integrate-and-fire neuron models.
364.  Origin of heterogeneous spiking patterns in spinal dorsal horn neurons (Balachandar & Prescott 2018)
"Neurons are often classified by spiking pattern. Yet, some neurons exhibit distinct patterns under subtly different test conditions, which suggests that they operate near an abrupt transition, or bifurcation. A set of such neurons may exhibit heterogeneous spiking patterns not because of qualitative differences in which ion channels they express, but rather because quantitative differences in expression levels cause neurons to operate on opposite sides of a bifurcation. Neurons in the spinal dorsal horn, for example, respond to somatic current injection with patterns that include tonic, single, gap, delayed and reluctant spiking. It is unclear whether these patterns reflect five cell populations (defined by distinct ion channel expression patterns), heterogeneity within a single population, or some combination thereof. We reproduced all five spiking patterns in a computational model by varying the densities of a low-threshold (KV1-type) potassium conductance and an inactivating (A-type) potassium conductance and found that single, gap, delayed and reluctant spiking arise when the joint probability distribution of those channel densities spans two intersecting bifurcations that divide the parameter space into quadrants, each associated with a different spiking pattern. ... "
365.  Oscillation and coding in a proposed NN model of insect olfaction (Horcholle-Bossavit et al. 2007)
"For the analysis of coding mechanisms in the insect olfactory system, a fully connected network of synchronously updated McCulloch and Pitts neurons (MC-P type) was (previously) developed. ... Considering the update time as an intrinsic clock, this “Dynamic Neural Filter” (DNF), which maps regions of input space into spatio-temporal sequences of neuronal activity, is able to produce exact binary codes extracted from the synchronized activities recorded at the level of projection neurons (PN) in the locust antennal lobe (AL) in response to different odors ... We find synaptic matrices which lead to both the emergence of robust oscillations and spatio-temporal patterns, using a formal criterion, based on a Normalized Euclidian Distance (NED), in order to measure the use of the temporal dimension as a coding dimension by the DNF. Similarly to biological PN, the activity of excitatory neurons in the model can be both phase-locked to different cycles of oscillations which (is reminiscent of the) local field potential (LFP), and nevertheless exhibit dynamic behavior complex enough to be the basis of spatio-temporal codes."
366.  Oscillations emerging from noise-driven NNs (Tchumatchenko & Clopath 2014)
" ... Here we show how the oscillation frequency is shaped by single neuron resonance, electrical and chemical synapses.The presence of both gap junctions and subthreshold resonance are necessary for the emergence of oscillations. Our results are in agreement with several experimental observations such as network responses to oscillatory inputs and offer a much-needed conceptual link connecting a collection of disparate effects observed in networks."
367.  Oscillations, phase-of-firing coding and STDP: an efficient learning scheme (Masquelier et al. 2009)
The model demonstrates how a common oscillatory drive for a group of neurons formats and reliabilizes their spike times - through an activation-to-phase conversion - so that repeating activation patterns can be easily detected and learned by a downstream neuron equipped with STDP, and then recognized in just one oscillation cycle.
368.  Oxytocin and VIP involvement in prolactin secretion (Egli et al. 2004,2006, Bertram et al. 2006)
"Prolactin (PRL) is secreted from lactotrophs of the anterior pituitary gland of rats in a unique pattern in response to uterine cervical stimulation (CS) during mating. Surges of PRL secretion occur in response to relief from hypothalamic dopaminergic inhibition and stimulation by hypothalamic releasing neurohormones. In this study, we characterized the role of oxytocin (OT) in this system and the involvement of vasoactive intestinal polypeptide (VIP) from the suprachiasmatic nucleus (SCN) in controlling OT and PRL secretion of CS rats. ... OT measurements of serial blood samples obtained from ovariectomized (OVX) CS rats displayed a prominent increase at the time of the afternoon PRL peak. The injection of VIP antisense oligonucleotides into the SCN abolished the afternoon increase of OT and PRL in CS-OVX animals. These findings suggest that VIP from the SCN contributes to the regulation of OT and PRL secretion in CS rats. We propose that in CS rats the regulatory mechanism(s) for PRL secretion comprise coordinated action of neuroendocrine dopaminergic and OT cells, both governed by the daily rhythm of VIP-ergic output from the SCN. This hypothesis is illustrated with a mathematical model."
369.  Pacemaker neurons and respiratory rhythm generation (Purvis et al 2007)
"The pre-Botzinger complex (pBC) is a vital subcircuit of the respiratory central pattern generator. Although the existence of neurons with pacemaker-like bursting properties in this network is not questioned, their role in network rhythmogenesis is unresolved. ... We modeled the parameter variability of experimental data from pBC bursting pacemaker and nonpacemaker neurons using a modified version of our previously developed pBC neuron and network models. ... " The paper contains network modeling results that are not represented in this model entry. Only the neuron models are included in this modeldb entry.
370.  Paired turbulence and light effect on calcium increase in Hermissenda (Blackwell 2004)
The sea slug Hermissenda learns to associate light and hair cell stimulation, but not when the stimuli are temporally uncorrelated...These issues were addressed using a multi-compartmental computer model of phototransduction, calcium dynamics, and ionic currents of the Hermissenda photoreceptor...simulations show that a potassium leak channel, which closes with an increase in calcium, is required to produce both the untrained LLD and the enhanced LLD due to the decrease in voltage dependent potassium currents.
371.  Pallidostriatal projections promote beta oscillations (Corbit, Whalen, et al 2016)
This model consists of an inhibitory loop combining the projections from GPe neurons back to the striatum (shown experimentally to predominantly affect fast spiking interneurons, FSIs), together with the coupling from FSIs to medium spiny neurons (MSNs) in the striatum, along with the projections from MSNs to GPe. All models are in the Hodgkin-Huxley formalism, adapted from previously published models for each cell type. The connected circuit produces irregular activity under control conditions, but increasing FSI-to-MSN connectivity as observed experimentally under dopamine depletion yields exaggerated beta oscillations and synchrony. Additional mechanistic aspects are also explored.
372.  Paradoxical GABA-mediated excitation (Lewin et al. 2012)
"GABA is the key inhibitory neurotransmitter in the adult central nervous system, but in some circumstances can lead to a paradoxical excitation that has been causally implicated in diverse pathologies from endocrine stress responses to diseases of excitability including neuropathic pain and temporal lobe epilepsy. We undertook a computational modeling approach to determine plausible ionic mechanisms of GABAA-dependent excitation in isolated post-synaptic CA1 hippocampal neurons because it may constitute a trigger for pathological synchronous epileptiform discharge. In particular, the interplay intracellular chloride accumulation via the GABAA receptor and extracellular potassium accumulation via the K/Cl co-transporter KCC2 in promoting GABAA-mediated excitation is complex. ..."
373.  Parallel odor processing by mitral and middle tufted cells in the OB (Cavarretta et al 2016, 2018)
"[...] experimental findings suggest that MC and mTC may encode parallel and complementary odor representations. We have analyzed the functional roles of these pathways by using a morphologically and physiologically realistic three-dimensional model to explore the MC and mTC microcircuits in the glomerular layer and deeper plexiform layers. [...]"
374.  Parallel STEPS: Large scale stochastic spatial reaction-diffusion simulat. (Chen & De Schutter 2017)
" ... In this paper, we describe an MPI-based, parallel operator-splitting implementation for stochastic spatial reaction-diffusion simulations with irregular tetrahedral meshes. The performance of our implementation is first examined and analyzed with simulations of a simple model. We then demonstrate its application to real-world research by simulating the reaction-diffusion components of a published calcium burst model in both Purkinje neuron sub-branch and full dendrite morphologies..."
375.  Parametric computation and persistent gamma in a cortical model (Chambers et al. 2012)
Using the Traub et al (2005) model of the cortex we determined how 33 synaptic strength parameters control gamma oscillations. We used fractional factorial design to reduce the number of runs required to 4096. We found an expected multiplicative interaction between parameters.
376.  Persistent Spiking in ACC Neurons (Ratte et al 2018)
"Neurons use action potentials, or spikes, to encode information. Some neurons can store information for short periods (seconds to minutes) by continuing to spike after a stimulus ends, thus enabling working memory. This so-called “persistent” spiking occurs in many brain areas and has been linked to activation of canonical transient receptor potential (TRPC) channels. However, TRPC activation alone is insufficient to explain many aspects of persistent spiking such as resumption of spiking after periods of imposed quiescence. Using experiments and simulations, we show that calcium influx caused by spiking is necessary and sufficient to activate TRPC channels and that the ensuing positive feedback interaction between intracellular calcium and TRPC channel activation can account for many hitherto unexplained aspects of persistent spiking."
377.  Persistent synchronized bursting activity in cortical tissues (Golomb et al 2005)
The program simulates a one-dimensional model of a cortical tissue with excitatory and inhibitory populations.
378.  Perturbation sensitivity implies high noise and suggests rate coding in cortex (London et al. 2010)
"... The network simulations were also based on a previously published model(Latham et al. 2000), but with modifications to allow the addition and detection of extra spikes (see Supplementary Information, section 7)."
379.  Phase locking in leaky integrate-and-fire model (Brette 2004)
"This shows the phase-locking structure of a LIF driven by a sinusoidal current. When the current crosses the threshold (a<3), the model almost always phase locks (in a measure-theoretical sense)."
380.  Phase oscillator models for lamprey central pattern generators (Varkonyi et al. 2008)
In our paper, Varkonyi et al. 2008, we derive phase oscillator models for the lamprey central pattern generator from two biophysically based segmental models. We study intersegmental coordination and show how these models can provide stable intersegmental phase lags observed in real animals.
381.  Phase plane reveals two slow variables in midbrain dopamine neuron bursts (Yu and Canavier, 2015)
"Midbrain dopamine neurons exhibit a novel type of bursting that we call “inverted square wave bursting” when exposed to Ca2+-activated small conductance (SK) K+ channel blockers in vitro. This type of bursting has three phases: hyperpolarized silence, spiking, and depolarization block. We find that two slow variables are required for this type of bursting, and we show that the three-dimensional bifurcation diagram for inverted square wave bursting is a folded surface with upper (depolarized) and lower (hyperpolarized) branches. ..."
382.  Phase precession through acceleration of local theta rhythm (Castro & Aguiar 2011)
"... Here we present a biophysical spiking model for phase precession in hippocampal CA1 which focuses on the interaction between place cells and local inhibitory interneurons. The model’s functional block is composed of a place cell (PC) connected with a local inhibitory cell (IC) which is modulated by the population theta rhythm. Both cells receive excitatory inputs from the entorhinal cortex (EC). ..."
383.  Phase response curve of a globus pallidal neuron (Fujita et al. 2011)
We investigated how changes in ionic conductances alter the phase response curve (PRC) of a globus pallidal (GP) neuron and stability of a synchronous activity of a GP network, using a single-compartmental conductance-based neuron model. The results showed the PRC and the stability were influenced by changes in the persistent sodium current, the Kv3 potassium, the M-type potassium and the calcium-dependent potassium current.
384.  Phase-locking analysis with transcranial magneto-acoustical stimulation (Yuan et al 2017)
"Transcranial magneto-acoustical stimulation (TMAS) uses ultrasonic waves and a static magnetic field to generate electric current in nerve tissues for the purpose of modulating neuronal activities. It has the advantage of high spatial resolution and penetration depth. Neuronal firing rhythms carry and transmit nerve information in neural systems. In this study, we investigated the phase-locking characteristics of neuronal firing rhythms with TMAS based on the Hodgkin-Huxley neuron model. The simulation results indicate that the modulation frequency of ultrasound can affect the phase-locking behaviors. The results of this study may help us to explain the potential firing mechanism of TMAS."
385.  Polychronization: Computation With Spikes (Izhikevich 2005)
"We present a minimal spiking network that can polychronize, that is, exhibit reproducible time-locked but not synchronous firing patterns with millisecond precision, as in synfire braids. The network consists of cortical spiking neurons with axonal conduction delays and spiketiming- dependent plasticity (STDP); a ready-to-use MATLAB code is included. It exhibits sleeplike oscillations, gamma (40 Hz) rhythms, conversion of firing rates to spike timings, and other interesting regimes. ... To our surprise, the number of coexisting polychronous groups far exceeds the number of neurons in the network, resulting in an unprecedented memory capacity of the system. ..."
386.  Population models of temporal differentiation (Tripp and Eliasmith 2010)
"Temporal derivatives are computed by a wide variety of neural circuits, but the problem of performing this computation accurately has received little theoretical study. Here we systematically compare the performance of diverse networks that calculate derivatives using cell-intrinsic adaptation and synaptic depression dynamics, feedforward network dynamics, and recurrent network dynamics. Examples of each type of network are compared by quantifying the errors they introduce into the calculation and their rejection of high-frequency input noise. ..."
387.  PreBotzinger Complex inspiratory neuron with NaP and CAN currents (Park and Rubin 2013)
We have built on earlier models to develop a single-compartment Hodgkin-Huxley type model incorporating NaP and CAN currents, both of which can play important roles in bursting of inspiratory neurons in the PreBotzinger Complex of the mammalian respiratory brain stem. The model tracks the evolution of membrane potential, related (in)activation variables, calcium concentration, and available fraction of IP3 channels. The model can produce several types of bursting, presented and analyzed from a dynamical systems perspective in our paper.
388.  Principles of Computational Modelling in Neuroscience (Book) (Sterratt et al. 2011)
"... This book provides a step-by-step account of how to model the neuron and neural circuitry to understand the nervous system at all levels, from ion channels to networks. Starting with a simple model of the neuron as an electrical circuit, gradually more details are added to include the effects of neuronal morphology, synapses, ion channels and intracellular signaling. The principle of abstraction is explained through chapters on simplifying models, and how simplified models can be used in networks. This theme is continued in a final chapter on modeling the development of the nervous system. Requiring an elementary background in neuroscience and some high school mathematics, this textbook is an ideal basis for a course on computational neuroscience."
389.  Prosthetic electrostimulation for information flow repair in a neocortical simulation (Kerr 2012)
This model is an extension of a model (<a href="http://senselab.med.yale.edu/ModelDB/ShowModel.asp?model=138379">138379</a>) recently published in Frontiers in Computational Neuroscience. This model consists of 4700 event-driven, rule-based neurons, wired according to anatomical data, and driven by both white-noise synaptic inputs and a sensory signal recorded from a rat thalamus. Its purpose is to explore the effects of cortical damage, along with the repair of this damage via a neuroprosthesis.
390.  PyPNS: Multiscale Simulation of a Peripheral Nerve in Python (Lubba et al 2018)
" ... To reduce experimentation load and allow for a faster, more detailed analysis of peripheral nerve stimulation and recording, computational models incorporating experimental insights will be of great help. We present a peripheral nerve simulator that combines biophysical axon models and numerically solved and idealised extracellular space models in one environment. We modelled the extracellular space as a three-dimensional resistive continuum governed by the electro-quasistatic approximation of the Maxwell equations. ..."
391.  Pyramidal Neuron Deep: Constrained by experiment (Dyhrfjeld-Johnsen et al. 2005)
"... As a practical demonstration of the use of CoCoDat we constructed a detailed computer model of an intrinsically bursting (IB) layer V pyramidal neuron from the rat barrel cortex supplementing experimental data (Schubert et al., 2001) with information extracted from the database. The pyramidal neuron morphology (Fig. 10B) was reconstructed from histological sections of a biocytin-stained IB neuron using the NeuroLucida software package..."
392.  Pyramidal neuron, fast, regular, and irregular spiking interneurons (Konstantoudaki et al 2014)
This is a model network of prefrontal cortical microcircuit based primarily on rodent data. It includes 16 pyramidal model neurons, 2 fast spiking interneuron models, 1 regular spiking interneuron model and 1 irregular spiking interneuron model. The goal of the paper was to use this model network to determine the role of specific interneuron subtypes in persistent activity
393.  Pyramidal Neuron: Deep, Thalamic Relay and Reticular, Interneuron (Destexhe et al 1998, 2001)
This package shows single-compartment models of different classes of cortical neurons, such as the "regular-spiking", "fast-spiking" and "bursting" (LTS) neurons. The mechanisms included are the Na+ and K+ currents for generating action potentials (INa, IKd), the T-type calcium current (ICaT), and a slow voltage-dependent K+ current (IM). See http://cns.fmed.ulaval.ca/alain_demos.html
394.  Pyramidal neurons switch from integrators to resonators (Prescott et al. 2008)
During wakefulness, pyramidal neurons in the intact brain are bombarded by synaptic input that causes tonic depolarization, increased membrane conductance (i.e. shunting), and noisy fluctuations in membrane potential; by comparison, pyramidal neurons in acute slices typically experience little background input. Such differences in operating conditions can compromise extrapolation of in vitro data to explain neuronal operation in vivo. ... in slice experiments, we show that CA1 hippocampal pyramidal cells switch from integrators to resonators, i.e. from class 1 to class 2 excitability. The switch is explained by increased outward current contributed by the M-type potassium current IM ... Thus, even so-called “intrinsic” properties may differ qualitatively between in vitro and in vivo conditions.
395.  Pyramidal neurons: IKHT offsets activation of IKLT to increase gain (Fernandez et al 2005)
This matlab model was supplied by Dr Fernandez. It provides the model specification for the below paper. The influence of a high threshold K current on low threshold K and Na currents (especially frequency-current relationships) are studied in the paper with both experiments and modeling. Please see the reference for more and details.
396.  Quantitative model of sleep-wake dynamics (Phillips & Robinson 2007)
"A quantitative, physiology-based model of the ascending arousal system is developed, using continuum neuronal population modeling, which involves averaging properties such as firing rates across neurons in each population. The model includes the ventrolateral preoptic area (VLPO), where circadian and homeostatic drives enter the system, the monoaminergic and cholinergic nuclei of the ascending arousal system, and their interconnections. The human sleep-wake cycle is governed by the activities of these nuclei, which modulate the behavioral state of the brain via diffuse neuromodulatory projections. ... The model behavior is robust across the constrained parameter ranges, but with sufficient flexibility to describe a wide range of observed phenomena. "
397.  Rapid desynchronization of an electrically coupled Golgi cell network (Vervaeke et al. 2010)
Electrical synapses between interneurons contribute to synchronized firing and network oscillations in the brain. However, little is known about how such networks respond to excitatory synaptic input. In addition to detailed electrophysiological recordings and histological investigations of electrically coupled Golgi cells in the cerebellum, a detailed network model of these cells was created. The cell models are based on reconstructed Golgi cell morphologies and the active conductances are taken from an earlier abstract Golgi cell model (Solinas et al 2007, accession no. 112685). Our results show that gap junction coupling can sometimes be inhibitory and either promote network synchronization or trigger rapid network desynchronization depending on the synaptic input. The model is available as a neuroConstruct project and can executable scripts can be generated for the NEURON simulator.
398.  Rat subthalamic projection neuron (Gillies and Willshaw 2006)
A computational model of the rat subthalamic nucleus projection neuron is constructed using electrophysiological and morphological data and a restricted set of channel specifications. The model cell exhibits a wide range of electrophysiological behaviors characteristic of rat subthalamic neurons. It reveals that a key set of three channels play a primary role in distinguishing behaviors: a high-voltage-activated calcium channel (Cav 1.2.-1.3), a low-voltage-activated calcium channel (Cav 3.-), and a small current calcium-activated potassium channel (KCa 2.1-2.3). See paper for more and details.
399.  Reconstrucing sleep dynamics with data assimilation (Sedigh-Sarvestani et al., 2012)
We have developed a framework, based on the unscented Kalman filter, for estimating hidden states and parameters of a network model of sleep. The network model includes firing rates and neurotransmitter output of 5 cell-groups in the rat brain.
400.  Recording from rod bipolar axon terminals in situ (Oltedal et al 2007)
"... Whole cell recordings from axon terminals and cell bodies were used to investigate the passive membrane properties of rod bipolar cells and analyzed with a two-compartment equivalent electrical circuit model developed by Mennerick et al. For both terminal- and soma-end recordings, capacitive current decays were well fitted by biexponential functions. Computer simulations of simplified models of rod bipolar cells demonstrated that estimates of the capacitance of the axon terminal compartment can depend critically on the recording location, with terminal-end recordings giving the best estimates. Computer simulations and whole cell recordings demonstrated that terminal-end recordings can yield more accurate estimates of the peak amplitude and kinetic properties of postsynaptic currents generated at the axon terminals due to increased electrotonic filtering of these currents when recorded at the soma. ..." See paper for more and details.
401.  Regulation of a slow STG rhythm (Nadim et al 1998)
Frequency regulation of a slow rhythm by a fast periodic input. Nadim, F., Manor, Y., Nusbaum, M. P., Marder, E. (1998) J. Neurosci. 18: 5053-5067
402.  Regulation of firing frequency in a midbrain dopaminergic neuron model (Kuznetsova et al. 2010)
A dopaminergic (DA) neuron model with a morphologicaly realistic dendritic architecture. The model captures several salient features of DA neurons under different pharmacological manipulations and exhibits depolarization block for sufficiently high current pulses applied to the soma.
403.  Regulation of the firing pattern in dopamine neurons (Komendantov et al 2004)
Midbrain dopaminergic (DA) neurons in vivo exhibit two major firing patterns: single-spike firing and burst firing. The firing pattern expressed is dependent on both the intrinsic properties of the neurons and their excitatory and inhibitory synaptic inputs. Experimental data suggest that the activation of NMDA and GABAA receptors is crucial contributor to the initiation and suppression of burst firing, respectively, and that blocking calcium-activated potassium channels can facilitate burst firing. This multi-compartmental model of a DA neuron with a branching structure was developed and calibrated based on in vitro experimental data to explore the effects of different levels of activation of NMDA and GABAA receptors as well as the modulation of the SK current on the firing activity.
404.  Relative spike time coding and STDP-based orientation selectivity in V1 (Masquelier 2012)
Phenomenological spiking model of the cat early visual system. We show how natural vision can drive spike time correlations on sufficiently fast time scales to lead to the acquisition of orientation-selective V1 neurons through STDP. This is possible without reference times such as stimulus onsets, or saccade landing times. But even when such reference times are available, we demonstrate that the relative spike times encode the images more robustly than the absolute ones.
405.  Reliability of Morris-Lecar neurons with added T, h, and AHP currents (Zeldenrust et al. 2013)
We investigated the reliability of the timing of spikes in a spike train in a Morris-Lecar model with several extensions. A frozen Gaussian noise current, superimposed on a DC current, was injected. The neuron responded with spike trains that showed trial-to-trial variability. The reliability depends on the shape (steepness) of the current input versus spike frequency output curve. The model also allowed to study the contribution of three relevant ionic membrane currents to reliability: a T-type calcium current, a cation selective h-current and a calcium dependent potassium current in order to allow bursting, investigate the consequences of a more complex current-frequency relation and produce realistic firing rates.
406.  Reproducibility and comparability of models for astrocyte Ca2+ excitability (Manninen et al 2017)
We tested the reproducibility and comparability of four astrocyte models (Manninen, Havela, Linne, 2017). We implemented and ran the python models using Jupyter Notebook. Model code produces results of Figure 1 and Figures 3-5 and partly Figure 2 in Manninen, Havela, Linne (2017).
407.  Reproducing infra-slow oscillations with dopaminergic modulation (Kobayashi et al 2017)
" ... In this paper, to reproduce ISO (Infra-Slow Oscillations) in neural networks, we show that dopaminergic modulation of STDP is essential. More specifically, we discovered a close relationship between two dopaminergic effects: modulation of the STDP function and generation of ISO. We therefore, numerically investigated the relationship in detail and proposed a possible mechanism by which ISO is generated."
408.  Respiratory central pattern generator network in mammalian brainstem (Rubin et al. 2009)
This model is a reduced version of a spatially organized respiratory central pattern generation network consisting of four neuronal populations (pre-I, early-I, post-I, and aug-E). In this reduction, each population is represented by a single neuron, in an activity-based framework (which includes the persistent sodium current for the pre-I population). The model includes three sources of external drive and can produce several experimentally observed rhythms.
409.  Respiratory pacemaker neurons (Butera et al 1999)
A network of oscillatory bursting neurons with excitatory coupling is hypothesized to define the primary kernel for respiratory rhythm generation in the pre-Botzinger complex (pre-BotC) in mammals. Two minimal models of these neurons are proposed. In model 1, bursting arises via fast activation and slow inactivation of a persistent Na current INaP-h. In model 2, bursting arises via a fast-activating persistent Na current INaP and slow activation of a K1 current IKS. In both models, action potentials are generated via fast Na and K currents. The two models have few differences in parameters to facilitate a rigorous comparison of the two different burst-generating mechanisms. Both models are consistent with many of the dynamic features of electrophysiological recordings from pre-BotC oscillatory bursting neurons in vitro, including voltage-dependent activity modes (silence, bursting, and beating), a voltage-dependent burst frequency that can vary from 0.05 to .1 Hz, and a decaying spike frequency during bursting. These results are robust and persist across a wide range of parameter values for both models. However, the dynamics of model 1 are more consistent with experimental data in that the burst duration decreases as the baseline membrane potential is depolarized and the model has a relatively flat membrane potential trajectory during the interburst interval. We propose several experimental tests to demonstrate the validity of either model and to differentiate between the two mechanisms.
410.  Resurgent Na+ current offers noise modulation in bursting neurons (Venugopal et al 2019)
"Neurons utilize bursts of action potentials as an efficient and reliable way to encode information. It is likely that the intrinsic membrane properties of neurons involved in burst generation may also participate in preserving its temporal features. Here we examined the contribution of the persistent and resurgent components of voltage-gated Na+ currents in modulating the burst discharge in sensory neurons. Using mathematical modeling, theory and dynamic-clamp electrophysiology, we show that, distinct from the persistent Na+ component which is important for membrane resonance and burst generation, the resurgent Na+ can help stabilize burst timing features including the duration and intervals. ..."
411.  Reverberatory bursts propagation and synchronization in developing cultured NNs (Huang et al 2016)
"Developing networks of neural systems can exhibit spontaneous, synchronous activities called neural bursts, which can be important in the organization of functional neural circuits. ... Using a propagation model we infer the spreading speed of the spiking activity, which increases as the culture ages. We perform computer simulations of the system using a physiological model of spiking networks in two spatial dimensions and find the parameters that reproduce the observed resynchronization of spiking in the bursts. An analysis of the simulated dynamics suggests that the depletion of synaptic resources causes the resynchronization. The spatial propagation dynamics of the simulations match well with observations over the course of a burst and point to an interplay of the synaptic efficacy and the noisy neural self-activation in producing the morphology of the bursts."
412.  Reward modulated STDP (Legenstein et al. 2008)
"... This article provides tools for an analytic treatment of reward-modulated STDP, which allows us to predict under which conditions reward-modulated STDP will achieve a desired learning effect. These analytical results imply that neurons can learn through reward-modulated STDP to classify not only spatial but also temporal firing patterns of presynaptic neurons. They also can learn to respond to specific presynaptic firing patterns with particular spike patterns. Finally, the resulting learning theory predicts that even difficult credit-assignment problems, where it is very hard to tell which synaptic weights should be modified in order to increase the global reward for the system, can be solved in a self-organizing manner through reward-modulated STDP. This yields an explanation for a fundamental experimental result on biofeedback in monkeys by Fetz and Baker. In this experiment monkeys were rewarded for increasing the firing rate of a particular neuron in the cortex and were able to solve this extremely difficult credit assignment problem. ... In addition our model demonstrates that reward-modulated STDP can be applied to all synapses in a large recurrent neural network without endangering the stability of the network dynamics."
413.  Rhesus Monkey Layer 3 Pyramidal Neurons: V1 vs PFC (Amatrudo, Weaver et al. 2012)
Whole-cell patch-clamp recordings and high-resolution 3D morphometric analyses of layer 3 pyramidal neurons in in vitro slices of monkey primary visual cortex (V1) and dorsolateral granular prefrontal cortex (dlPFC) revealed that neurons in these two brain areas possess highly distinctive structural and functional properties. ... Three-dimensional reconstructions of V1 and dlPFC neurons were incorporated into computational models containing Hodgkin-Huxley and AMPA- and GABAA-receptor gated channels. Morphology alone largely accounted for observed passive physiological properties, but led to AP firing rates that differed more than observed empirically, and to synaptic responses that opposed empirical results. Accordingly, modeling predicts that active channel conductances differ between V1 and dlPFC neurons. The unique features of V1 and dlPFC neurons are likely fundamental determinants of area-specific network behavior. The compact electrotonic arbor and increased excitability of V1 neurons support the rapid signal integration required for early processing of visual information. The greater connectivity and dendritic complexity of dlPFC neurons likely support higher level cognitive functions including working memory and planning.
414.  Robust modulation of integrate-and-fire models (Van Pottelbergh et al 2018)
"By controlling the state of neuronal populations, neuromodulators ultimately affect behavior. A key neuromodulation mechanism is the alteration of neuronal excitability via the modulation of ion channel expression. This type of neuromodulation is normally studied with conductance-based models, but those models are computationally challenging for large-scale network simulations needed in population studies. This article studies the modulation properties of the multiquadratic integrate-and-fire model, a generalization of the classical quadratic integrate-and-fire model. The model is shown to combine the computational economy of integrate-and-fire modeling and the physiological interpretability of conductance-based modeling. It is therefore a good candidate for affordable computational studies of neuromodulation in large networks."
415.  Robust Reservoir Generation by Correlation-Based Learning (Yamazaki & Tanaka 2008)
"Reservoir computing (RC) is a new framework for neural computation. A reservoir is usually a recurrent neural network with fixed random connections. In this article, we propose an RC model in which the connections in the reservoir are modifiable. ... We apply our RC model to trace eyeblink conditioning. The reservoir bridged the gap of an interstimulus interval between the conditioned and unconditioned stimuli, and a readout neuron was able to learn and express the timed conditioned response."
416.  Role for short term plasticity and OLM cells in containing spread of excitation (Hummos et al 2014)
This hippocampus model was developed by matching experimental data, including neuronal behavior, synaptic current dynamics, network spatial connectivity patterns, and short-term synaptic plasticity. Furthermore, it was constrained to perform pattern completion and separation under the effects of acetylcholine. The model was then used to investigate the role of short-term synaptic depression at the recurrent synapses in CA3, and inhibition by basket cell (BC) interneurons and oriens lacunosum-moleculare (OLM) interneurons in containing the unstable spread of excitatory activity in the network.
417.  Role of active dendrites in rhythmically-firing neurons (Goldberg et al 2006)
"The responsiveness of rhythmically-firing neurons to synaptic inputs is characterized by their phase response curve (PRC), which relates how weak somatic perturbations affect the timing of the next action potential. The shape of the somatic PRC is an important determinant of collective network dynamics. Here we study theoretically and experimentally the impact of distally-located synapses and dendritic nonlinearities on the synchronization properties of rhythmically firing neurons. Combining the theories of quasi-active cables and phase-coupled oscillators we derive an approximation for the dendritic responsiveness, captured by the neuron's dendritic PRC (dPRC). This closed-form expression indicates that the dPRCs are linearly-filtered versions of the somatic PRC, and that the filter characteristics are determined by the passive and active properties of the dendrite. ... collective dynamics can be qualitatively different depending on the location of the synapse, the neuronal firing rates and the dendritic nonlinearities." See paper for more and details.
418.  Role of afferent-hair cell connectivity in determining spike train regularity (Holmes et al 2017)
"Vestibular bouton afferent terminals in turtle utricle can be categorized into four types depending on their location and terminal arbor structure: lateral extrastriolar (LES), striolar, juxtastriolar, and medial extrastriolar (MES). The terminal arbors of these afferents differ in surface area, total length, collecting area, number of boutons, number of bouton contacts per hair cell, and axon diameter (Huwe JA, Logan CJ, Williams B, Rowe MH, Peterson EH. J Neurophysiol 113: 2420 –2433, 2015). To understand how differences in terminal morphology and the resulting hair cell inputs might affect afferent response properties, we modeled representative afferents from each region, using reconstructed bouton afferents. ..."
419.  Role of Ih in firing patterns of cold thermoreceptors (Orio et al., 2012)
" ... Here we investigated the role of Ih in cold-sensitive (CS) nerve endings, where cold sensory transduction actually takes place. Corneal CS nerve endings in mice show a rhythmic spiking activity at neutral skin temperature that switches to bursting mode when the temperature is lowered. ... Mathematical modeling shows that the firing phenotype of CS nerve endings from HCN1-/- mice can be reproduced by replacing HCN1 channels with the slower HCN2 channels rather than by abolishing Ih. We propose that Ih carried by HCN1 channels helps tune the frequency of the oscillation and the length of bursts underlying regular spiking in cold thermoreceptors, having important implications for neural coding of cold sensation. "
420.  Role of KCNQ1 and IKs in cardiac repolarization (Silva, Rudy 2005)
Detailed Markov models of IKs (the slow delayed rectifier K+ current) and its alpha-subunit KCNQ1 were developed. The model is compared to experiment in the paper. The role of IKs in disease and drug treatments is elucidated (the prevention of excessive action potential prolongation and development of arrhythmogenic early afterdepolarizations). See paper for more and details.
421.  Role of KCNQ1 and IKs in cardiac repolarization (Silva, Rudy 2005) (XPP)
Detailed Markov model of IKs (the slow delayed rectifier K+ current) is supplied here in XPP. The model is compared to experiment in the paper. The role of IKs in disease and drug treatments is elucidated (the prevention of excessive action potential prolongation and development of arrhythmogenic early afterdepolarizations). See also modeldb accession number 55748 code and reference for more and details. This XPP version of the model reproduces Figure 3C in the paper by default. These model files were submitted by: Dr. Sheng-Nan Wu, Han-Dong Chang, Jiun-Shian Wu Department of Physiology National Cheng Kung University Medical College
422.  Role of the AIS in the control of spontaneous frequency of dopaminergic neurons (Meza et al 2017)
Computational modeling showed that the size of the Axon Initial Segment (AIS), but not its position within the somatodendritic domain, is the major causal determinant of the tonic firing rate in the intact model, by virtue of the higher intrinsic frequency of the isolated AIS. Further mechanistic analysis of the relationship between neuronal morphology and firing rate showed that dopaminergic neurons function as a coupled oscillator whose frequency of discharge results from a compromise between AIS and somatodendritic oscillators.
423.  S cell network (Moss et al 2005)
Excerpts from the abstract: S cells form a chain of electrically coupled neurons that extends the length of the leech CNS and plays a critical role in sensitization during whole-body shortening. ... Serotonin ... increasedAP latency across the electrical synapse, suggesting that serotonin reduced coupling between S cells. ... Serotonin modulated instantaneous AP frequency when APs were initiated in separate S cells and in a computational model of S cell activity following mechanosensory input. Thus, serotonergic modulation of S cell electrical synapses may contribute to changes in the pattern of activity in the S cell network. See paper for more.
424.  Salamander retinal ganglian cells: morphology influences firing (Sheasby, Fohlmeister 1999)
Nerve impulse entrainment and other excitation and passive phenomena are analyzed for a morphologically diverse and exhaustive data set (n=57) of realistic (3-dimensional computer traced) soma-dendritic tree structures of ganglion cells in the tiger salamander (Ambystoma tigrinum) retina.
425.  Salamander retinal ganglion cell: ion channels (Fohlmeister, Miller 1997)
A realistic five (5) channel spiking model reproduces the bursting behavior of tiger salamander ganglion cells in the retina. Please see the readme for more information.
426.  Scaling self-organizing maps to model large cortical networks (Bednar et al 2004)
Self-organizing computational models with specific intracortical connections can explain many functional features of visual cortex, such as topographic orientation and ocular dominance maps. ... This article introduces two techniques that make large simulations practical. First, we show how parameter scaling equations can be derived for laterally connected self-organizing models. These equations result in quantitatively equivalent maps over a wide range of simulation sizes, making it possible to debug small simulations and then scale them up only when needed. ... Second, we use parameter scaling to implement a new growing map method called GLISSOM, which dramatically reduces the memory and computational requirements of large self-organizing networks. See paper for more and details.
427.  SCZ-associated variant effects on L5 pyr cell NN activity and delta osc. (Maki-Marttunen et al 2018)
" … Here, using computational modeling, we show that a common biomarker of schizophrenia, namely, an increase in delta-oscillation power, may be a direct consequence of altered expression or kinetics of voltage-gated ion channels or calcium transporters. Our model of a circuit of layer V pyramidal cells highlights multiple types of schizophrenia-related variants that contribute to altered dynamics in the delta frequency band. Moreover, our model predicts that the same membrane mechanisms that increase the layer V pyramidal cell network gain and response to delta-frequency oscillations may also cause a decit in a single-cell correlate of the prepulse inhibition, which is a behavioral biomarker highly associated with schizophrenia."
428.  Self-organized olfactory pattern recognition (Kaplan & Lansner 2014)
" ... We present a large-scale network model with single and multi-compartmental Hodgkin–Huxley type model neurons representing olfactory receptor neurons (ORNs) in the epithelium, periglomerular cells, mitral/tufted cells and granule cells in the olfactory bulb (OB), and three types of cortical cells in the piriform cortex (PC). Odor patterns are calculated based on affinities between ORNs and odor stimuli derived from physico-chemical descriptors of behaviorally relevant real-world odorants. ... The PC was implemented as a modular attractor network with a recurrent connectivity that was likewise organized through Hebbian–Bayesian learning. We demonstrate the functionality of the model in a one-sniff-learning and recognition task on a set of 50 odorants. Furthermore, we study its robustness against noise on the receptor level and its ability to perform concentration invariant odor recognition. Moreover, we investigate the pattern completion capabilities of the system and rivalry dynamics for odor mixtures."
429.  Sensitivity of noisy neurons to coincident inputs (Rossant et al. 2011)
"Two distant or coincident spikes are injected into a noisy balanced leaky integrate-and-fire neuron. The PSTH of the neuron in response to these inputs is calculated along with the extra number of spikes in the two cases. This number is higher for the coincident spikes, showing the sensitivity of a noisy neuron to coincident inputs."
430.  Sensory feedback in an oscillatory interference model of place cell activity (Monaco et al. 2011)
Many animals use a form of dead reckoning known as 'path integration' to maintain a sense of their location as they explore the world. However, internal motion signals and the neural activity that integrates them can be noisy, leading inevitably to inaccurate position estimates. The rat hippocampus and entorhinal cortex support a flexible system of spatial representation that is critical to spatial learning and memory. The position signal encoded by this system is thought to rely on path integration, but it must be recalibrated by familiar landmarks to maintain accuracy. To explore the interaction between path integration and external landmarks, we present a model of hippocampal activity based on the interference of theta-frequency oscillations that are modulated by realistic animal movements around a track. We show that spatial activity degrades with noise, but introducing external cues based on direct sensory feedback can prevent this degradation. When these cues are put into conflict with each other, their interaction produces a diverse array of response changes that resembles experimental observations. Feedback driven by attending to landmarks may be critical to navigation and spatial memory in mammals.
431.  Simulated light response in rod photoreceptors (Liu and Kourennyi 2004)
We developed a complete computer model of the rod, which accurately reproduced the main features of the light response and allowed us to demonstrate that it was suppression of Kx channels that was essential for slowing SLR and increasing excitability of rods. The results reported in this work further establish the importance of Kx channels in rod photoreceptor function.
432.  Simulating ion channel noise in an auditory brainstem neuron model (Schmerl & McDonnell 2013)
" ... Here we demonstrate that biophysical models of channel noise can give rise to two kinds of recently discovered stochastic facilitation effects in a Hodgkin-Huxley-like model of auditory brainstem neurons. The first, known as slope-based stochastic resonance (SBSR), enables phasic neurons to emit action potentials that can encode the slope of inputs that vary slowly relative to key time constants in the model. The second, known as inverse stochastic resonance (ISR), occurs in tonically firing neurons when small levels of noise inhibit tonic firing and replace it with burstlike dynamics. ..." Preprint available at http://arxiv.org/abs/1311.2643
433.  Simulation studies on mechanisms of levetiracetam-mediated inhibition of IK(DR) (Huang et al. 2009)
Levetiracetam (LEV) is an S-enantiomer pyrrolidone derivative with established antiepileptic efficacy in generalized epilepsy and partial epilepsy. However, its effects on ion currents and membrane potential remain largely unclear. In this study, we investigated the effect of LEV on differentiated NG108-15 neurons. ... Simulation studies in a modified Hodgkin-Huxley neuron and network unraveled that the reduction of slowly inactivating IK(DR) resulted in membrane depolarization accompanied by termination of the firing of action potentials in a stochastic manner. Therefore, the inhibitory effects on slowly inactivating IK(DR) (Kv3.1-encoded current) may constitute one of the underlying mechanisms through which LEV affects neuronal activity in vivo.
434.  Simulation study of Andersen-Tawil syndrome (Sung et al 2006)
Patients with Andersen-Tawil syndrome (ATS) mostly have mutations on the KCNJ2 gene producing loss of function or dominant-negative suppression of the inward rectifier K(+) channel Kir2.1. However, clinical manifestations of ATS including dysmorphic features, periodic paralysis (hypo-, hyper-, or normokalemic), long QT, and ventricular arrhythmias (VA) are considerably variable. Using a modified dynamic Luo-Rudy simulation model of cardiac ventricular myocyte, we elucidate the mechanisms of VA in ATS. We adopted a kinetic model of KCNJ2 in which channel block by Mg(+2) and spermine was incorporated. In this study, we attempt to examine the effects of KCNJ2 mutations on the ventricular action potential (AP), single-channel Markovian models were reformulated and incorporated into the dynamic Luo-Rudy model for rapidly and slowly delayed rectifying K(+) currents and KCNJ2 channel. During pacing at 1.0 Hz with [K(+)]o at 5.4 mM, a stepwise 10% reduction of Kir2.1 channel conductance progressively prolonged the terminal repolarization phase of AP along with gradual depolarization of the resting membrane potential (RMP). At 90% reduction, early after- depolarizations (EADs) became inducible and RMP was depolarized to -55.0 mV (control: -90.1 mV) followed by emergence of spontaneous action potentials (SAP). Both EADs and SAP were facilitated by a decrease in [K(+)]o and suppressed by increase in [K(+)]o. beta-adrenergic stimulation enhanced delayed after-depolarizations (DADs) and could also facilitate EADs as well as SAP in the setting of low [K(+)]o and reduced Kir2.1 channel conductance. In conclusion, the spectrum of VA in ATS includes (1) triggered activity mediated by EADs and/or DADs, and (2) abnormal automaticity manifested as SAP. These VA can be aggravated by a decrease in [K(+)]o and beta-adrenergic stimulation, and may potentially induce torsades de pointes and cause sudden death. In patients with ATS, the hypokalemic form of periodic paralysis should have the highest propensity to VA especially during physical activities.
435.  Simulation system of spinal cord motor nuclei and assoc. nerves and muscles (Cisi and Kohn 2008)
A Web-based simulation system of the spinal cord circuitry responsible for muscle control is described. The simulator employs two-compartment motoneuron models for S, FR and FF types, with synaptic inputs acting through conductance variations. Four motoneuron pools with their associated interneurons are represented in the simulator, with the possibility of inclusion of more than 2,000 neurons and 2,000,000 synapses. ... Inputs to the motoneuron pool come from populations of interneurons (Ia reciprocal inhibitory interneurons, Ib interneurons, and Renshaw cells) and from stochastic point processes associated with descending tracts. ... The generation of the H-reflex by the Ia-motoneuron pool system and its modulation by spinal cord interneurons is included in the simulation system.
436.  Simulations of motor unit discharge patterns (Powers et al. 2011)
" ... To estimate the potential contributions of PIC (Persistent Inward Current) activation and synaptic input patterns to motor unit discharge patterns, we examined the responses of a set of cable motoneuron models to different patterns of excitatory and inhibitory inputs. The models were first tuned to approximate the current- and voltage-clamp responses of low- and medium-threshold spinal motoneurons studied in decerebrate cats and then driven with different patterns of excitatory and inhibitory inputs. The responses of the models to excitatory inputs reproduced a number of features of human motor unit discharge. However, the pattern of rate modulation was strongly influenced by the temporal and spatial pattern of concurrent inhibitory inputs. Thus, even though PIC activation is likely to exert a strong influence on firing rate modulation, PIC activation in combination with different patterns of excitatory and inhibitory synaptic inputs can produce a wide variety of motor unit discharge patterns."
437.  Simulations of oscillations in piriform cortex (Wilson & Bower 1992)
"1. A large-scale computer model of the piriform cortex was constructed on the basis of the known anatomic and physiological organization of this region. 2. The oscillatory field potential and electroencephalographic (EEG) activity generated by the model was compared with actual physiological results. The model was able to produce patterns of activity similar to those recorded physiologically in response to both weak and strong electrical shocks to the afferent input. The model also generated activity patterns similar to EEGs recorded in behaving animals. 3. ..."
438.  Single neuron with dynamic ion concentrations (Cressman et al. 2009)
These are the full and reduced models of a generic single neuron with dynamic ion concentrations as described in Cressman et al., Journal of Computational Neuroscience (2009) 26:159–170.
439.  Sleep deprivation in the ascending arousal system (Phillips & Robinson 2008)
"A physiologically based quantitative model of the human ascending arousal system is used to study sleep deprivation after being calibrated on a small set of experimentally based criteria. The model includes the sleep–wake switch of mutual inhibition between nuclei which use monoaminergic neuromodulators, and the ventrolateral preoptic area. The system is driven by the circadian rhythm and sleep homeostasis. We use a small number of experimentally derived criteria to calibrate the model for sleep deprivation, then investigate model predictions for other experiments, demonstrating the scope of application. ... The form of the homeostatic drive suggests that periods of wake during recovery from sleep deprivation are phases of relative recovery, in the sense that the homeostatic drive continues to converge toward baseline levels. This undermines the concept of sleep debt, and is in agreement with experimentally restricted recovery protocols. Finally, we compare our model to the two-process model, and demonstrate the power of physiologically based modeling by correctly predicting sleep latency times following deprivation from experimental data. "
440.  Sleep-wake transitions in corticothalamic system (Bazhenov et al 2002)
The authors investigate the transition between sleep and awake states with intracellular recordings in cats and computational models. The model describes many essential features of slow wave sleep and activated states as well as the transition between them.
441.  Slow wave propagation in the guinea-pig gastric antrum (Hirst et al. 2006, Edwards and Hirst 2006)
"(Edwards and Hirst 2006) provides an electrical description of the propagation of slow waves and pacemaker potentials in the guinea-pig gastric antrum in anal and circumferential directions. As electrical conduction between laterally adjacent circular muscle bundles is regularly interrupted, anal conduction of pacemaker potentials was assumed to occur via an electrically interconnected chain of myenteric interstitial cells of Cajal (ICCMY). ICCMY were also connected resistively to serially connected compartments of longitudinal muscle. Circumferential conduction occurred in a circular smooth muscle bundle that was represented as a chain of electrically connected isopotential compartments: each compartment contained a proportion of intramuscular interstitial cells of Cajal (ICCIM) that are responsible for the regenerative component of the slow wave. The circular muscle layer, which contains ICCIM, and the ICCMY network incorporated a mechanism, modelled as a two-stage chemical reaction, which produces an intracellular messenger. ... The model generates pacemaker potentials and slow waves with propagation velocities similar to those determined in the physiological experiments described in the accompanying paper."
442.  Small world networks of Type I and Type II Excitable Neurons (Bogaard et al. 2009)
Implemented with NEURON 5.9, four model neurons with varying excitability properties affect the spatiotemporal patterning of small world networks of homogeneous and heterogeneous cell population.
443.  Software for teaching neurophysiology of neuronal circuits (Grisham et al. 2008)
"To circumvent the many problems in teaching neurophysiology as a “wet lab,” we developed SWIMMY, a virtual fish that swims by moving its virtual tail by means of a virtual neural circuit. ... Using SWIMMY, students (1) review the basics of neurophysiology, (2) identify the neurons in the circuit, (3) ascertain the neurons’ synaptic interconnections, (4) discover which cells generate the motor pattern of swimming, (5) discover how the rhythm is generated, and finally (6) use an animation that corresponds to the activity of the motoneurons to discover the behavioral effects produced by various lesions and explain them in terms of their neural underpinnings. ..."
444.  Sparsely connected networks of spiking neurons (Brunel 2000)
The dynamics of networks of sparsely connected excitatory and inhibitory integrate-and-fire neurons are studied analytically (and with simulations). The analysis reveals a rich repertoire of states, including synchronous states in which neurons fire regularly; asynchronous states with stationary global activity and very irregular individual cell activity; and states in which the global activity oscillates but individual cells fire irregularly, typically at rates lower than the global oscillation frequency. See paper for more and details.
445.  Spike burst-pause dynamics of Purkinje cells regulate sensorimotor adaptation (Luque et al 2019)
"Cerebellar Purkinje cells mediate accurate eye movement coordination. However, it remains unclear how oculomotor adaptation depends on the interplay between the characteristic Purkinje cell response patterns, namely tonic, bursting, and spike pauses. Here, a spiking cerebellar model assesses the role of Purkinje cell firing patterns in vestibular ocular reflex (VOR) adaptation. The model captures the cerebellar microcircuit properties and it incorporates spike-based synaptic plasticity at multiple cerebellar sites. ..."
446.  Spike trains in Hodgkin–Huxley model and ISIs of acupuncture manipulations (Wang et al. 2008)
The Hodgkin-Huxley equations (HH) are parameterized by a number of parameters and shows a variety of qualitatively different behaviors depending on the parameter values. Under stimulation of an external periodic voltage, the ISIs (interspike intervals) of a HH model are investigated in this work, while the frequency of the voltage is taken as the controlling parameter. As well-known, the science of acupuncture and moxibustion is an important component of Traditional Chinese Medicine with a long history. Although there are a number of different acupuncture manipulations, the method for distinguishing them is rarely investigated. With the idea of ISI, we study the electrical signal time series at the spinal dorsal horn produced by three different acupuncture manipulations in Zusanli point and present an effective way to distinguish them.
447.  Spike-Timing-Based Computation in Sound Localization (Goodman and Brette 2010)
" ... In neuron models consisting of spectro-temporal filtering and spiking nonlinearity, we found that the binaural structure induced by spatialized sounds is mapped to synchrony patterns that depend on source location rather than on source signal. Location-specific synchrony patterns would then result in the activation of location-specific assemblies of postsynaptic neurons. We designed a spiking neuron model which exploited this principle to locate a variety of sound sources in a virtual acoustic environment using measured human head-related transfer functions. ..."
448.  Spikes,synchrony,and attentive learning by laminar thalamocort. circuits (Grossberg & Versace 2007)
"... The model hereby clarifies, for the first time, how the following levels of brain organization coexist to realize cognitive processing properties that regulate fast learning and stable memory of brain representations: single cell properties, such as spiking dynamics, spike-timing-dependent plasticity (STDP), and acetylcholine modulation; detailed laminar thalamic and cortical circuit designs and their interactions; aggregate cell recordings, such as current-source densities and local field potentials; and single cell and large-scale inter-areal oscillations in the gamma and beta frequency domains. ..."
449.  Spiking GridPlaceMap model (Pilly & Grossberg, PLoS One, 2013)
Development of spiking grid cells and place cells in the entorhinal-hippocampal system to represent positions in large spaces
450.  Spiking neuron model of the basal ganglia (Humphries et al 2006)
A spiking neuron model of the basal ganglia (BG) circuit (striatum, STN, GP, SNr). Includes: parallel anatomical channels; tonic dopamine; dopamine receptors in striatum, STN, and GP; burst-firing in STN; GABAa, AMPA, and NMDA currents; effects of synaptic location. Model demonstrates selection and switching of input signals. Replicates experimental data on changes in slow-wave (<1 Hz) and gamma-band oscillations within BG nuclei following lesions and pharmacological manipulations.
451.  Spontaneous calcium oscillations in astrocytes (Lavrentovich and Hemkin 2008)
" ... We propose here a mathematical model of how spontaneous Ca2+ oscillations arise in astrocytes. This model uses the calcium-induced calcium release and inositol cross-coupling mechanisms coupled with a receptor-independent method for producing inositol (1,4,5)-trisphosphate as the heart of the model. By computationally mimicking experimental constraints we have found that this model provides results that are qualitatively similar to experiment."
452.  Spontaneous calcium oscillations in single astrocytes (Riera et al. 2011) (Manninen et al 2017)
We tested the reproducibility and comparability of four astrocyte models (Manninen, Havela, Linne, 2017). Model by Riera et al. (2011) was one of them. We implemented and ran the model by Riera et al. (2011) using Jupyter Notebook. Model codes produce results of Figures 1 and 2 in Manninen, Havela, Linne (2017).
453.  Spontaneous firing caused by stochastic channel gating (Chow, White 1996)
NEURON implementation of model of stochastic channel gating, resulting in spontaneous firing. Qualitatively reproduces the phenomena described in the reference.
454.  State-dependent rhythmogenesis in a half-center locomotor CPG (Ausburn et al 2017)
"The spinal locomotor central pattern generator (CPG) generates rhythmic activity with alternating flexion and extension phases. This rhythmic pattern is likely to result from inhibitory interactions between neural populations representing flexor and extensor half-centers. However, it is unclear whether the flexor-extensor CPG has a quasi-symmetric organization with both half-centers critically involved in rhythm generation, features an asymmetric organization with flexor-driven rhythmogenesis, or comprises a pair of intrinsically rhythmic half-centers. There are experimental data that support each of the above concepts but appear to be inconsistent with the others. In this theoretical/modeling study, we present and analyze a CPG model architecture that can operate in different regimes consistent with the above three concepts depending on conditions, which are defined by external excitatory drives to CPG half-centers. We show that control of frequency and phase durations within each regime depends on network dynamics, defined by the regime-dependent expression of the half-centers' intrinsic rhythmic capabilities and the operating phase transition mechanisms (escape vs. release). Our study suggests state dependency in locomotor CPG operation and proposes explanations for seemingly contradictory experimental data."
455.  STD-dependent and independent encoding of Input irregularity as spike rate (Luthman et al. 2011)
"... We use a conductance-based model of a CN neuron to study the effect of the regularity of Purkinje cell spiking on CN neuron activity. We find that increasing the irregularity of Purkinje cell activity accelerates the CN neuron spike rate and that the mechanism of this recoding of input irregularity as output spike rate depends on the number of Purkinje cells converging onto a CN neuron. ..."
456.  STDP allows fast rate-modulated coding with Poisson-like spike trains (Gilson et al. 2011)
The model demonstrates that a neuron equipped with STDP robustly detects repeating rate patterns among its afferents, from which the spikes are generated on the fly using inhomogenous Poisson sampling, provided those rates have narrow temporal peaks (10-20ms) - a condition met by many experimental Post-Stimulus Time Histograms (PSTH).
457.  STDP and NMDAR Subunits (Gerkin et al. 2007)
The paper argues for competing roles of NR2A- and NR2B-containing NMDARs in spike-timing-dependent plasticity. This simple dynamical model recapitulates the results of STDP experiments involving selective blockers of NR2A- and NR2B-containing NMDARs, for which the stimuli are pre- and postsynaptic spikes in varying combinations. Experiments were done using paired recordings from glutamatergic neurons in rat hippocampal cultures. This model focuses on the dynamics of the putative potentiation and depression modules themselves, and their interaction For detailed dynamics involving NMDARs and Ca2+ transients, see Rubin et al., J. Neurophys., 2005.
458.  STDP depends on dendritic synapse location (Letzkus et al. 2006)
This model was published in Letzkus, Kampa & Stuart (2006) J Neurosci 26(41):10420-9. The simulation creates several plots showing voltage and NMDA current and conductance changes at different apical dendritic locations in layer 5 pyramidal neurons during STDP induction protocols. Created by B. Kampa (2006).
459.  Stochastic and periodic inputs tune ongoing oscillations (Hutt et al. 2016)
" ... We here analyze a network of recurrently connected spiking neurons with time delay displaying stable synchronous dynamics. Using mean-field and stability analyses, we investigate the influence of dynamic inputs on the frequency of firing rate oscillations. ..."
460.  Strategy for kinase transport by microtubules to nerve terminals (Koon et al. 2014)
This model was used in the computational study of the strategies of protein transport in the context of JNK (c-JUN NH2-terminal kinase) transport along microtubules to the terminals of neuronal cells. Diffusion governs the first strategy. In the second strategy, proteins of the JNK signaling cascade bind to scaffolds and the whole protein-scaffold cargo is transported by kinesin motors along microtubules. Using the results from the simulations, the two distinct strategies for transport were compared.
461.  Striatal GABAergic microcircuit, dopamine-modulated cell assemblies (Humphries et al. 2009)
To begin identifying potential dynamically-defined computational elements within the striatum, we constructed a new three-dimensional model of the striatal microcircuit's connectivity, and instantiated this with our dopamine-modulated neuron models of the MSNs and FSIs. A new model of gap junctions between the FSIs was introduced and tuned to experimental data. We introduced a novel multiple spike-train analysis method, and apply this to the outputs of the model to find groups of synchronised neurons at multiple time-scales. We found that, with realistic in vivo background input, small assemblies of synchronised MSNs spontaneously appeared, consistent with experimental observations, and that the number of assemblies and the time-scale of synchronisation was strongly dependent on the simulated concentration of dopamine. We also showed that feed-forward inhibition from the FSIs counter-intuitively increases the firing rate of the MSNs.
462.  Striatal GABAergic microcircuit, spatial scales of dynamics (Humphries et al, 2010)
The main thrust of this paper was the development of the 3D anatomical network of the striatum's GABAergic microcircuit. We grew dendrite and axon models for the MSNs and FSIs and extracted probabilities for the presence of these neurites as a function of distance from the soma. From these, we found the probabilities of intersection between the neurites of two neurons given their inter-somatic distance, and used these to construct three-dimensional striatal networks. These networks were examined for their predictions for the distributions of the numbers and distances of connections for all the connections in the microcircuit. We then combined the neuron models from a previous model (Humphries et al, 2009; ModelDB ID: 128874) with the new anatomical model. We used this new complete striatal model to examine the impact of the anatomical network on the firing properties of the MSN and FSI populations, and to study the influence of all the inputs to one MSN within the network.
463.  Striatal NN model of MSNs and FSIs investigated effects of dopamine depletion (Damodaran et al 2015)
This study investigates the mechanisms that are affected in the striatal network after dopamine depletion and identifies potential therapeutic targets to restore normal activity.
464.  Structure-dynamics relationships in bursting neuronal networks revealed (Mäki-Marttunen et al. 2013)
This entry includes tools for generating and analyzing network structure, and for running the neuronal network simulations on them.
465.  Studies of stimulus parameters for seizure disruption using NN simulations (Anderson et al. 2007)
Architecturally realistic neocortical model using seven classes of excitatory and inhibitory single compartment Hodgkin-Huxley cells. Wiring is adapted to minicolumn hypothesis and incorporates visual and neocortical data. Simulation demonstrates spontaneous bursting onset and cessation, and activity can be altered with external electric field.
466.  Study of augmented Rubin and Terman 2004 deep brain stim. model in Parkinsons (Pascual et al. 2006)
" ... The model by Rubin and Terman [31] represents one of the most comprehensive and biologically plausible models of DBS published recently. We examined the validity of the model, replicated its simulations and tested its robustness. While our simulations partially reproduced the results presented by Rubin and Terman [31], several issues were raised including the high complexity of the model in its non simplified form, the lack of robustness of the model with respect to small perturbations, the nonrealistic representation of the thalamus and the absence of time delays. Computational models are indeed necessary, but they may not be sufficient in their current forms to explain the effect of chronic electrical stimulation on the activity of the basal ganglia (BG) network in PD."
467.  Subiculum network model with dynamic chloride/potassium homeostasis (Buchin et al 2016)
This is the code implementing the single neuron and spiking neural network dynamics. The network has the dynamic ion concentrations of extracellular potassium and intracellular chloride. The code contains multiple parameter variations to study various mechanisms of the neural excitability in the context of chloride homeostasis.
468.  Sympathetic neuron (Wheeler et al 2004)
This study shows how synaptic convergence and plasticity can interact to generate synaptic gain in autonomic ganglia and thereby enhance homeostatic control. Using a conductance-based computational model of an idealized sympathetic neuron, we simulated the postganglionic response to noisy patterns of presynaptic activity and found that a threefold amplification in postsynaptic spike output can arise in ganglia, depending on the number and strength of nicotinic synapses, the presynaptic firing rate, the extent of presynaptic facilitation, and the expression of muscarinic and peptidergic excitation. See references for details.
469.  Sympathetic Preganglionic Neurone (Briant et al. 2014)
A model of a sympathetic preganglionic neurone of muscle vasoconstrictor-type.
470.  Synaptic gating at axonal branches, and sharp-wave ripples with replay (Vladimirov et al. 2013)
The computational model of in vivo sharp-wave ripples with place cell replay. Excitatory post-synaptic potentials at dendrites gate antidromic spikes arriving from the axonal collateral, and thus determine when the soma and the main axon fire. The model allows synchronous replay of pyramidal cells during sharp-wave ripple event, and the replay is possible in both forward and reverse directions.
471.  Synaptic information transfer in computer models of neocortical columns (Neymotin et al. 2010)
"... We sought to measure how the activity of the network alters information flow from inputs to output patterns. Information handling by the network reflected the degree of internal connectivity. ... With greater connectivity strength, the recurrent network translated activity and information due to contribution of activity from intrinsic network dynamics. ... At still higher internal synaptic strength, the network corrupted the external information, producing a state where little external information came through. The association of increased information retrieved from the network with increased gamma power supports the notion of gamma oscillations playing a role in information processing."
472.  Synaptic plasticity can produce and enhance direction selectivity (Carver et al, 2008)
" ... We propose a parsimonious model of motion processing that generates direction selective responses using short-term synaptic depression and can reproduce salient features of direction selectivity found in a population of neurons in the midbrain of the weakly electric fish Eigenmannia virescens. The model achieves direction selectivity with an elementary Reichardt motion detector: information from spatially separated receptive fields converges onto a neuron via dynamically different pathways. In the model, these differences arise from convergence of information through distinct synapses that either exhibit or do not exhibit short-term synaptic depression—short-term depression produces phase-advances relative to nondepressing synapses. ..."
473.  Synchronization by D4 dopamine receptor-mediated phospholipid methylation (Kuznetsova, Deth 2008)
"We describe a new molecular mechanism of dopamine-induced membrane protein modulation that can tune neuronal oscillation frequency to attention related gamma rhythm. This mechanism is based on the unique ability of D4 dopamine receptors (D4R) to carry out phospholipid methylation (PLM) that may affect the kinetics of ion channels. We show that by deceasing the inertia of the delayed rectifier potassium channel, a transition to 40 Hz oscillations can be achieved. ..."
474.  Synchronized oscillations of clock gene expression in the choroid plexus (Myung et al 2018)
Our model simulates synchronized rhythms in the clock gene expression found in the choroid plexus. These synchronized oscillations, primarily mediated by gap junctions, showed interesting relationships between their amplitude, oscillation frequency, and coupling strength (gap junction density) in our experimental data. The model is based on coupled Poincaré oscillators and replicates this phenomenon via a non-zero "twist" in each cell.
475.  Synchrony by synapse location (McTavish et al. 2012)
This model considers synchrony between mitral cells induced via shared granule cell interneurons while taking into account the spatial constraints of the system. In particular, since inhibitory inputs decay passively along the lateral dendrites, this model demonstrates that an optimal arrangement of the inhibitory synapses will be near the cell bodies of the relevant mitral cells.
476.  Synthesis of spatial tuning functions from theta cell spike trains (Welday et al., 2011)
A single compartment model reproduces the firing rate maps of place, grid, and boundary cells by receiving inhibitory inputs from theta cells. The theta cell spike trains are modulated by the rat's movement velocity in such a way that phase interference among their burst pattern creates spatial envelope function which simulate the firing rate maps.
477.  Systems-level modeling of neuronal circuits for leech swimming (Zheng et al. 2007)
"This paper describes a mathematical model of the neuronal central pattern generator (CPG) that controls the rhythmic body motion of the swimming leech. The systems approach is employed to capture the neuronal dynamics essential for generating coordinated oscillations of cell membrane potentials by a simple CPG architecture with a minimal number of parameters. ... parameter estimation leads to predictions regarding the synaptic coupling strength and intrinsic period gradient along the nerve cord, the latter of which agrees qualitatively with experimental observations."
478.  T-type Ca current in thalamic neurons (Wang et al 1991)
A model of the transient, low-threshold voltage-dependent (T-type) Ca2+ current is constructed using whole-cell voltage-clamp data from enzymatically isolated rat thalamocortical relay neurons. The T-type Ca2+ current is described according to the Hodgkin-Huxley scheme, using the m3h format, with rate constants determined from the experimental data.
479.  Temperature-Dependent Pyloric Pacemaker Kernel (Caplan JS et al., 2014)
"... Here we demonstrate that biophysical models of channel noise can give rise to two kinds of recently discovered stochastic facilitation effects in a Hodgkin-Huxley-like model of auditory brainstem neurons. The first, known as slope-based stochastic resonance (SBSR), enables phasic neurons to emit action potentials that can encode the slope of inputs that vary slowly relative to key time constants in the model. The second, known as inverse stochastic resonance (ISR), occurs in tonically firing neurons when small levels of noise inhibit tonic firing and replace it with burstlike dynamics. ... our results show that possible associated computational benefits may occur due to channel noise in neurons of the auditory brainstem. ... "
480.  Temporal decorrelation by intrinsic cellular dynamics (Wang et al 2003)
"... Recent investigations in primary visual (V1) cortical neurons have demonstrated that adaptation to prolonged changes in stimulus contrast is mediated in part through intrinsic ionic currents, a Ca2+ activated K+ current (IKCa) and especially a Na+ activated K+ current (IKNa). The present study was designed to test the hypothesis that the activation of adaptation ionic currents may provide a cellular mechanism for temporal decorrelation in V1. A conductance-based neuron model was simulated, which included an IKCa and an IKNa. We show that the model neuron reproduces the adaptive behavior of V1 neurons in response to high contrast inputs. ...". See paper for details and more.
481.  Temporal integration by stochastic recurrent network (Okamoto et al. 2007)
"Temporal integration of externally or internally driven information is required for a variety of cognitive processes. This computation is generally linked with graded rate changes in cortical neurons, which typically appear during a delay period of cognitive task in the prefrontal and other cortical areas. Here, we present a neural network model to produce graded (climbing or descending) neuronal activity. Model neurons are interconnected randomly by AMPA-receptor–mediated fast excitatory synapses and are subject to noisy background excitatory and inhibitory synaptic inputs. In each neuron, a prolonged afterdepolarizing potential follows every spike generation. Then, driven by an external input, the individual neurons display bimodal rate changes between a baseline state and an elevated firing state, with the latter being sustained by regenerated afterdepolarizing potentials. ..."
482.  Thalamic interneuron multicompartment model (Zhu et al. 1999)
This is an attempt to recreate a set of simulations originally performed in 1994 under NEURON version 3 and last tested in 1999. When I ran it now it did not behave exactly the same as previously which I suspect is due to some minor mod file changes on my side rather than due to any differences among versions. After playing around with the parameters a little bit I was able to get something that looks generally like a physiological trace in J Neurophysiol, 81:702--711, 1999, fig. 8b top trace. This sad preface is simply offered in order to encourage anyone who is interested in this model to make and post fixes. I'm happy to help out. Simulation by JJ Zhu To run nrnivmodl nrngui.hoc
483.  Thalamic network model of deep brain stimulation in essential tremor (Birdno et al. 2012)
"... Thus the decreased effectiveness of temporally irregular DBS trains is due to long pauses in the stimulus trains, not the degree of temporal irregularity alone. We also conducted computer simulations of neuronal responses to the experimental stimulus trains using a biophysical model of the thalamic network. Trains that suppressed tremor in volunteers also suppressed fluctuations in thalamic transmembrane potential at the frequency associated with cerebellar burst-driver inputs. Clinical and computational findings indicate that DBS suppresses tremor by masking burst-driver inputs to the thalamus and that pauses in stimulation prevent such masking. Although stimulation of other anatomic targets may provide tremor suppression, we propose that the most relevant neuronal targets for effective tremor suppression are the afferent cerebellar fibers that terminate in the thalamus."
484.  Thalamic neuron: Modeling rhythmic neuronal activity (Meuth et al. 2005)
The authors use an in vitro cell model of a single acutely isolated thalamic neuron in the NEURON simulation environment to address and discuss questions in an undergraduate course. Topics covered include passive electrical properties, composition of action potentials, trains of action potentials, multicompartment modeling, and research topics. The paper includes detailed instructions on how to run the simulations in the appendix.
485.  Thalamic quiescence of spike and wave seizures (Lytton et al 1997)
A phase plane analysis of a two cell interaction between a thalamocortical neuron (TC) and a thalamic reticularis neuron (RE).
486.  Thalamic Reticular Network (Destexhe et al 1994)
Demo for simulating networks of thalamic reticular neurons (reproduces figures from Destexhe A et al 1994)
487.  Thalamic reticular neurons: the role of Ca currents (Destexhe et al 1996)
The experiments and modeling reported in this paper show how intrinsic bursting properties of RE cells may be explained by dendritic calcium currents.
488.  Thalamocortical and Thalamic Reticular Network (Destexhe et al 1996)
NEURON model of oscillations in networks of thalamocortical and thalamic reticular neurons in the ferret. (more applications for a model quantitatively identical to previous DLGN model; updated for NEURON v4 and above)
489.  Thalamocortical augmenting response (Bazhenov et al 1998)
In the cortical model, augmenting responses were more powerful in the "input" layer compared with those in the "output" layer. Cortical stimulation of the network model produced augmenting responses in cortical neurons in distant cortical areas through corticothalamocortical loops and low-threshold intrathalamic augmentation. ... The predictions of the model were compared with in vivo recordings from neurons in cortical area 4 and thalamic ventrolateral nucleus of anesthetized cats. The known intrinsic properties of thalamic cells and thalamocortical interconnections can account for the basic properties of cortical augmenting responses. See reference for details. NEURON implementation note: cortical SU cells are getting slightly too little stimulation - reason unknown.
490.  Thalamocortical model of spike and wave seizures (Suffczynski et al. 2004)
SIMULINK macroscopic model of transitions between normal (spindle) activity and spike and wave (SW) discharges in the thalamocortical network. The model exhibits bistability properties and stochastic fluctuations present in the network may flip the system between the two operational states. The predictions of the model were compared with real EEG data in rats and humans. A possibility to abort an ictal state by a single counter stimulus is suggested by the model.
491.  Thalamocortical Relay cell under current clamp in high-conductance state (Zeldenrust et al 2018)
Mammalian thalamocortical relay (TCR) neurons switch their firing activity between a tonic spiking and a bursting regime. In a combined experimental and computational study, we investigated the features in the input signal that single spikes and bursts in the output spike train represent and how this code is influenced by the membrane voltage state of the neuron. Identical frozen Gaussian noise current traces were injected into TCR neurons in rat brain slices to adjust, fine-tune and validate a three-compartment TCR model cell (Destexhe et al. 1998, accession number 279). Three currents were added: an h-current (Destexhe et al. 1993,1996, accession number 3343), a high-threshold calcium current and a calcium- activated potassium current (Huguenard & McCormick 1994, accession number 3808). The information content carried by the various types of events in the signal as well as by the whole signal was calculated. Bursts phase-lock to and transfer information at lower frequencies than single spikes. On depolarization the neuron transits smoothly from the predominantly bursting regime to a spiking regime, in which it is more sensitive to high-frequency fluctuations. Finally, the model was used to in the more realistic “high-conductance state” (Destexhe et al. 2001, accession number 8115), while being stimulated with a Poisson input (Brette et al. 2007, Vogels & Abbott 2005, accession number 83319), where fluctuations are caused by (synaptic) conductance changes instead of current injection. Under “standard” conditions bursts are difficult to initiate, given the high degree of inactivation of the T-type calcium current. Strong and/or precisely timed inhibitory currents were able to remove this inactivation.
492.  Thalamocortical relay neuron models constrained by experiment and optimization (Iavarone et al 2019)
493.  The activity phase of postsynaptic neurons (Bose et al 2004)
We show, in a simplified network consisting of an oscillator inhibiting a follower neuron, how the interaction between synaptic depression and a transient potassium current in the follower neuron determines the activity phase of this neuron. We derive a mathematical expression to determine at what phase of the oscillation the follower neuron becomes active. This expression can be used to understand which parameters determine the phase of activity of the follower as the frequency of the oscillator is changed. See paper for more.
494.  The dynamics underlying pseudo-plateau bursting in a pituitary cell model (Teka et al. 2011)
" ... pseudo-plateau bursts, are unlike bursts studied mathematically in neurons (plateau bursting) and the standard fast-slow analysis used for plateau bursting is of limited use. Using an alternative fast-slow analysis, with one fast and two slow variables, we show that pseudo-plateau bursting is a canard-induced mixed mode oscillation. ..." See paper for other results.
495.  The neocortical microcircuit collaboration portal (Markram et al. 2015)
"This portal provides an online public resource of the Blue Brain Project's first release of a digital reconstruction of the microcircuitry of juvenile Rat somatosensory cortex, access to experimental data sets used in the reconstruction, and the resulting models."
496.  The relationship between two fast/slow analysis techniques for bursting oscill. (Teka et al. 2012)
"Bursting oscillations in excitable systems reflect multi-timescale dynamics. These oscillations have often been studied in mathematical models by splitting the equations into fast and slow subsystems. Typically, one treats the slow variables as parameters of the fast subsystem and studies the bifurcation structure of this subsystem. This has key features such as a z-curve (stationary branch) and a Hopf bifurcation that gives rise to a branch of periodic spiking solutions. In models of bursting in pituitary cells, we have recently used a different approach that focuses on the dynamics of the slow subsystem. Characteristic features of this approach are folded node singularities and a critical manifold. … We find that the z-curve and Hopf bifurcation of the twofast/ one-slow decomposition are closely related to the voltage nullcline and folded node singularity of the one-fast/two-slow decomposition, respectively. They become identical in the double singular limit in which voltage is infinitely fast and calcium is infinitely slow."
497.  The role of ATP-sensitive potassium channels in a hippocampal neuron (Huang et al. 2007)
"Hyperglycemia-related neuronal excitability and epileptic seizures are not uncommon in clinical practice. However, their underlying mechanism remains elusive. ATP-sensitive K(+) (K(ATP)) channels are found in many excitable cells, including cardiac myocytes, pancreatic beta cells, and neurons. These channels provide a link between the electrical activity of cell membranes and cellular metabolism. We investigated the effects of higher extracellular glucose on hippocampal K(ATP) channel activities and neuronal excitability. The cell-attached patch-clamp configuration on cultured hippocampal cells and a novel multielectrode recording system on hippocampal slices were employed. In addition, a simulation modeling hippocampal CA3 pyramidal neurons (Pinsky-Rinzel model) was analyzed to investigate the role of K(ATP) channels in the firing of simulated action potentials. ..."
498.  Theta phase precession in a model CA3 place cell (Baker and Olds 2007)
"... The present study concerns a neurobiologically based computational model of the emergence of theta phase precession in which the responses of a single model CA3 pyramidal cell are examined in the context of stimulation by realistic afferent spike trains including those of place cells in entorhinal cortex, dentate gyrus, and other CA3 pyramidal cells. Spike-timing dependent plasticity in the model CA3 pyramidal cell leads to a spatially correlated associational synaptic drive that subsequently creates a spatially asymmetric expansion of the model cell’s place field. ... Through selective manipulations of the model it is possible to decompose theta phase precession in CA3 into the separate contributing factors of inheritance from upstream afferents in the dentate gyrus and entorhinal cortex, the interaction of synaptically controlled increasing afferent drive with phasic inhibition, and the theta phase difference between dentate gyrus granule cell and CA3 pyramidal cell activity."
499.  Tonic firing in substantia gelatinosa neurons (Melnick et al 2004)
Ionic conductances underlying excitability in tonically firing neurons (TFNs) from substantia gelatinosa (SG) were studied by the patch-clamp method in rat spinal cord slices. ... Suppression of Ca2+ and KCA currents ... did not abolish the basic pattern of tonic firing, indicating that it was generated by voltage-gated Na+ and K+ currents. ... on the basis of present data, we created a model of TFN and showed that Na+ and KDR currents are sufficient to generate a basic pattern of tonic firing. It is concluded that the balanced contribution of all ionic conductances described here is important for generation and modulation of tonic firing in SG neurons. See paper for more and details.
500.  Tonic neuron in spinal lamina I: prolongation of subthreshold depol. (Prescott and De Koninck 2005)
Model demonstrates mechanism whereby two kinetically distinct inward currents act synergistically to prolong subthreshold depolarization. The important currents are a persistent Na current (with fast kinetics) and a persistent Ca current (with slower kinetics). Model also includes a slow K current and transient Ca current, in addition to standard HH currents. Model parameters are set to values used in Fig. 8A. Simulation shows prolonged depolarizations in response to two brief stimuli.
501.  Touch Sensory Cells (T Cells) of the Leech (Cataldo et al. 2004) (Scuri et al. 2007)
Bursts of spikes in leech T cells produce an AHP, which results from activation of a Na+/K+ pump and a Ca2+-dependent K+ current. Activity-dependent increases in the AHP are believed to induce conduction block of spikes in several regions of the neuron, which in turn, may decrease presynaptic invasion of spikes and thereby decrease transmitter release. To explore this possibility, we used the neurosimulator SNNAP to develop a multi-compartmental model of the T cell. Each compartment was modeled as an equivalent electrical circuit, in which some currents were regulated by intracellular Ca2+ and Na+. The membrane model consisted of a membrane capacitance (Cm), for which we used the value 1 uF/cm2, in parallel with two inward currents (Na+ and Ca2+), two K+ currents, a leak current and pump current. The model incorporated empirical data that describe the geometry of the cell and activity-dependent changes of the AHP (see paper for details). Simulations indicated that at some branching points, activity-dependent increases of the AHP reduced the number of spikes transmitted from the minor receptive field to the soma and beyond. These results suggest that the AHP can regulate spike conduction within the presynaptic arborizations of the cell and could in principle contribute to the synaptic depression that is correlated with increases in the AHP.
502.  Towards a virtual C. elegans (Palyanov et al. 2012)
"... Here we present a detailed demonstration of a virtual C. elegans aimed at integrating these data in the form of a 3D dynamic model operating in a simulated physical environment. Our current demonstration includes a realistic flexible worm body model, muscular system and a partially implemented ventral neural cord. Our virtual C. elegans demonstrates successful forward and backward locomotion when sending sinusoidal patterns of neuronal activity to groups of motor neurons. ..."
503.  TRPM8-dependent dynamic response in cold thermoreceptors (Olivares et al. 2015)
This model reproduces the dynamic response of cold thermoreceptors, transiently changing the firing rate upon heating or cooling. It also displays the 'static' or adapted firing patterns observed in these receptors.
504.  Turtle visual cortex model (Nenadic et al. 2003, Wang et al. 2005, Wang et al. 2006)
This is a model of the visual cortex of freshwater turtles that is based upon the known anatomy and physiology of individual neurons. The model was published in three papers (Nenadic et al., 2003; Wang et al., 2005; Wang et al., 2006), which should be consulted for full details on its construction. The model has also been used in several papers (Robbins and Senseman, 2004; Du et al., 2005; Du et al., 2006). It is implemented in GENESIS (Bower and Beeman, 1998).
505.  Two-cell inhibitory network bursting dynamics captured in a one-dimensional map (Matveev et al 2007)
" ... Here we describe a simple method that allows us to investigate the existence and stability of anti-phase bursting solutions in a network of two spiking neurons, each possessing a T-type calcium current and coupled by reciprocal inhibition. We derive a one-dimensional map which fully characterizes the genesis and regulation of anti-phase bursting arising from the interaction of the T-current properties with the properties of synaptic inhibition. ..."
506.  Understanding how fast activating K+ channels promote bursting in pituitary cells (Vo et al 2014)
"... Experimental observations have shown ... that fast-activating voltage- and calcium-dependent potassium (BK) current tends to promote bursting in pituitary cells. This burst promoting effect requires fast activation of the BK current, otherwise it is inhibitory to bursting. In this work, we analyze a pituitary cell model in order to answer the question of why the BK activation must be fast to promote bursting. ..."
507.  Updated Tritonia Swim CPG (Calin-Jagemann et al. 2007)
Model of the 3-cell core CPG (DSI, C2, and VSI-B) mediating escape swimming in Tritonia diomedea. Cells use a hybrid integrate-and-fire scheme pioneered by Peter Getting. Each model cell is reconstructed from extensive physiological measurements to precisely mimic I-F curves, synaptic waveforms, and functional connectivity.
508.  Using Strahler`s analysis to reduce realistic models (Marasco et al, 2013)
Building on our previous work (Marasco et al., (2012)), we present a general reduction method based on Strahler's analysis of neuron morphologies. We show that, without any fitting or tuning procedures, it is possible to map any morphologically and biophysically accurate neuron model into an equivalent reduced version. Using this method for Purkinje cells, we demonstrate how run times can be reduced up to 200-fold, while accurately taking into account the effects of arbitrarily located and activated synaptic inputs.
509.  Ventricular cell model (Luo Rudy dynamic model) (Luo Rudy 1994) used in (Wang et al 2006) (XPP)
A mathematical model of the membrane action potential of the mammalian ventricular cell introduced in Luo, Rudy 1991 and used in Wang et al 2006 is made available here in XPP. The model is based, whenever possible, on recent single-cell and single-channel data and incorporates the possibility of changing extracellular potassium concentration [K]o. ... The results are consistent with recent experimental observations, and the model simulations relate these phenomena to the underlying ionic channel kinetics. See papers for more and details.
510.  Vertical System (VS) tangential cells network model (Trousdale et al. 2014)
Network model of the VS tangential cell system, with 10 cells per hemisphere. Each cell is a two compartment model with one compartment for dendrites and one for the axon. The cells are coupled through axonal gap junctions. The code allows to simulate responses of the VS network to a variety of visual stimuli to investigate coding as a function of gap junction strength.
511.  Vibration-sensitive Honeybee interneurons (Ai et al 2017)
"Female honeybees use the “waggle dance” to communicate the location of nectar sources to their hive mates. Distance information is encoded in the duration of the waggle phase (von Frisch, 1967). During the waggle phase, the dancer produces trains of vibration pulses, which are detected by the follower bees via Johnston's organ located on the antennae. To uncover the neural mechanisms underlying the encoding of distance information in the waggle dance follower, we investigated morphology, physiology, and immunohistochemistry of interneurons arborizing in the primary auditory center of the honeybee (Apis mellifera). We identified major interneuron types, named DL-Int-1, DL-Int-2, and bilateral DL-dSEG-LP, that responded with different spiking patterns to vibration pulses applied to the antennae. Experimental and computational analyses suggest that inhibitory connection plays a role in encoding and processing the duration of vibration pulse trains in the primary auditory center of the honeybee."
512.  Visual Cortex Neurons: Dendritic computations (Archie, Mel 2000)
Neuron and C program files from Archie, K.A. and Mel, B.W. A model of intradendritic computation of binocular disparity. Nature Neuroscience 3:54-63, 2000 The original files for this model are located at the web site http://www-lnc.usc.edu/~karchie/synmap
513.  Visual physiology of the layer 4 cortical circuit in silico (Arkhipov et al 2018)
"Despite advances in experimental techniques and accumulation of large datasets concerning the composition and properties of the cortex, quantitative modeling of cortical circuits under in-vivo-like conditions remains challenging. Here we report and publicly release a biophysically detailed circuit model of layer 4 in the mouse primary visual cortex, receiving thalamo- cortical visual inputs. The 45,000-neuron model was subjected to a battery of visual stimuli, and results were compared to published work and new in vivo experiments. ..."
514.  VTA dopamine neuron (Tarfa, Evans, and Khaliq 2017)
In our model of a midbrain VTA dopamine neuron, we show that the decay kinetics of the A-type potassium current can control the timing of rebound action potentials.
515.  VTA neurons: Morphofunctional alterations in acute opiates withdrawal (Enrico et al. 2016)
" ... Here we present a biophysical model of a DA VTA neuron based on 3D morphological reconstruction and electrophysiological data, showing how opiates withdrawal-driven morphological and electrophysiological changes could affect the firing rate and discharge pattern...."
516.  Wang-Buzsaki Interneuron (Talathi et al., 2010)
The submitted code provides the relevant C++ files, matlabfiles and the data files essential to reproduce the figures in the JCNS paper titled Control of neural synchrony using channelrhodopsin-2: A computational study.
517.  Zebrafish Mauthner-cell model (Watanabe et al 2017)
The NEURON model files encode the channel generator and firing simulator for simulating development and differentiation of the Mauthner cell (M-cell) excitability in zebrafish. The channel generator enables us to generate arbitrary Na+ and K+ channels by changing parameters of a Hodgkin-Huxley model under emulation of two-electrode voltage-clamp recordings in Xenopus oocyte system. The firing simulator simulates current-clamp recordings to generate firing patterns of the model M-cell, which are implemented with arbitrary-generated basic Na+ and K+ conductances and low-threshold K+ channels Kv7.4/KCNQ4 and sole Kv1.1 or Kv1.1 coexpressed with Kvbeta2.

Re-display model names without descriptions