Models that contain the Model Concept : Oscillations

(The model is used to investigate oscillations in membrane potential and/or activity patterns.)
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.  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.
3.  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. ..."
4.  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.
5.  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.
6.  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.
7.  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. ..."
8.  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."
9.  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."
10.  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)
11.  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."
12.  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.
13.  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.
14.  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.
15.  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.
16.  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.
17.  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."
18.  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. ..."
19.  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.
20.  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.
21.  An oscillatory neural autoencoder based on frequency modulation and multiplexing (Soman et al 2018)
" ... We propose here an oscillatory neural network model that performs the function of an autoencoder. The model is a hybrid of rate-coded neurons and neural oscillators. Input signals modulate the frequency of the neural encoder oscillators. These signals are then multiplexed using a network of rate-code neurons that has afferent Hebbian and lateral anti-Hebbian connectivity, termed as Lateral Anti Hebbian Network (LAHN). Finally the LAHN output is de-multiplexed using an output neural layer which is a combination of adaptive Hopf and Kuramoto oscillators for the signal reconstruction. The Kuramoto-Hopf combination performing demodulation is a novel way of describing a neural phase-locked loop. The proposed model is tested using both synthetic signals and real world EEG signals. The proposed model arises out of the general motivation to construct biologically inspired, oscillatory versions of some of the standard neural network models, and presents itself as an autoencoder network based on oscillatory neurons applicable to time series signals. As a demonstration, the model is applied to compression of EEG signals."
22.  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.
23.  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.
24.  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.
25.  Beta-cell hubs maintain Ca2+ oscillations in human and mouse islet simulations (Lei et al 2018)
"Islet ß-cells are responsible for secreting all circulating insulin in response to rising plasma glucose concentrations. These cells are a phenotypically diverse population that express great functional heterogeneity. In mice, certain ß-cells (termed 'hubs') have been shown to be crucial for dictating the islet response to high glucose, with inhibition of these hub cells abolishing the coordinated Ca2+ oscillations necessary for driving insulin secretion. These ß-cell hubs were found to be highly metabolic and susceptible to pro-inflammatory and glucolipotoxic insults. In this study, we explored the importance of hub cells in human by constructing mathematical models of Ca2+ activity in human islets. Our simulations revealed that hubs dictate the coordinated Ca2+ response in both mouse and human islets; silencing a small proportion of hubs abolished whole-islet Ca2+ activity. We also observed that if hubs are assumed to be preferentially gap junction coupled, then the simulations better adhere to the available experimental data. Our simulations of 16 size-matched mouse and human islet architectures revealed that there are species differences in the role of hubs; ..."
26.  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.
27.  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.
28.  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.
29.  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. ..."
30.  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.
31.  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. ..."
32.  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.
33.  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. ..."
34.  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.
35.  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.
36.  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.
37.  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).
38.  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. ..."
39.  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. ..."
40.  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.
41.  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.
42.  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. …"
43.  Cellular classes revealed by heartbeat-related modulation of extracellular APs (Mosher et al 2020)
"Determining cell types is critical for understanding neural circuits but remains elusive in the living human brain. Current approaches discriminate units into putative cell classes using features of the extracellular action potential (EAP); in absence of ground truth data, this remains a problematic procedure. We find that EAPs in deep structures of the brain exhibit robust and systematic variability during the cardiac cycle. These cardiac-related features refine neural classification. We use these features to link bio-realistic models generated from in vitro human whole-cell recordings of morphologically classified neurons to in vivo recordings. We differentiate aspiny inhibitory and spiny excitatory human hippocampal neurons and, in a second stage, demonstrate that cardiac-motion features reveal two types of spiny neurons with distinct intrinsic electrophysiological properties and phase-locking characteristics to endogenous oscillations. This multi-modal approach markedly improves cell classification in humans, offers interpretable cell classes, and is applicable to other brain areas and species."
44.  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. ..."
45.  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."
46.  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.
47.  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.
48.  Cerebellar stellate cells: changes in threshold, latency and frequency of firing (Mitry et al 2020)
"Cerebellar stellate cells are inhibitory molecular interneurons that regulate the firing properties of Purkinje cells, the sole output of cerebellar cortex. Recent evidence suggests that these cells exhibit temporal increase in excitability during whole-cell patch-clamp configuration in a phenomenon termed runup. They also exhibit a non-monotonic first-spike latency profile as a function of the holding potential in response to a fixed step-current. In this study, we use modeling approaches to unravel the dynamics of runup and categorize the firing behavior of cerebellar stellate cells as either type I or type II oscillators. We then extend this analysis to investigate how the non-monotonic latency profile manifests itself during runup. We employ a previously developed, but revised, Hodgkin–Huxley type model to show that stellate cells are indeed type I oscillators possessing a saddle node on an invariant cycle (SNIC) bifurcation. The SNIC in the model acts as a “threshold” for tonic firing and produces a slow region in the phase space called the ghost of the SNIC. The model reveals that (i) the SNIC gets left-shifted during runup with respect to I app = I test in the current-step protocol, and (ii) both the distance from the stable limit cycle along with the slow region produce the non-monotonic latency profile as a function of holding potential. Using the model, we elucidate how latency can be made arbitrarily large for a specific range of holding potentials close to the SNIC during pre-runup (post-runup). We also demonstrate that the model can produce transient single spikes in response to step- currents entirely below I SNIC , and that a pair of dynamic inhibitory and excitatory post- synaptic inputs can robustly evoke action potentials, provided that the magnitude of the inhibition is either low or high but not intermediate. Our results show that the topology of the SNIC is the key to explaining such behaviors."
49.  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. …"
50.  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. …"
51.  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).
52.  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.
53.  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."
54.  Complex dynamics: reproducing Golgi cell electroresponsiveness (Geminiani et al 2018, 2019ab)
Excerpts from three papers abstracts: "Brain neurons exhibit complex electroresponsive properties – including intrinsic subthreshold oscillations and pacemaking, resonance and phase-reset – which are thought to play a critical role in controlling neural network dynamics. Although these properties emerge from detailed representations of molecular-level mechanisms in “realistic” models, they cannot usually be generated by simplified neuronal models (although these may show spike-frequency adaptation and bursting). We report here that this whole set of properties can be generated by the extended generalized leaky integrate-and-fire (E-GLIF) neuron model. ..." "... In order to reproduce these properties in single-point neuron models, we have optimized the Extended-Generalized Leaky Integrate and Fire (E-GLIF) neuron through a multi-objective gradient-based algorithm targeting the desired input–output relationships. ..." " ... In order to investigate how single neuron dynamics and geometrical modular connectivity affect cerebellar processing, we have built an olivocerebellar Spiking Neural Network (SNN) based on a novel simplification algorithm for single point models (Extended Generalized Leaky Integrate and Fire, EGLIF) capturing essential non-linear neuronal dynamics (e.g., pacemaking, bursting, adaptation, oscillation and resonance). ..."
55.  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.
56.  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.
57.  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.
58.  Computational model of the distributed representation of operant reward memory (Costa et al. 2020)
Operant reward learning of feeding behavior in Aplysia increases the frequency and regularity of biting, as well as biases buccal motor patterns (BMPs) toward ingestion-like BMPs (iBMPs). The engram underlying this memory comprises cells that are part of a central pattern generating (CPG) circuit and includes increases in the intrinsic excitability of identified cells B30, B51, B63, and B65, and increases in B63–B30 and B63–B65 electrical synaptic coupling. To examine the ways in which sites of plasticity (individually and in combination) contribute to memory expression, a model of the CPG was developed. The model included conductance-based descriptions of cells CBI-2, B4, B8, B20, B30, B31, B34, B40, B51, B52, B63, B64, and B65, and their synaptic connections. The model generated patterned activity that resembled physiological BMPs, and implementation of the engram reproduced increases in frequency, regularity, and bias. Combined enhancement of B30, B63, and B65 excitabilities increased BMP frequency and regularity, but not bias toward iBMPs. Individually, B30 increased regularity and bias, B51 increased bias, B63 increased frequency, and B65 decreased all three BMP features. Combined synaptic plasticity contributed primarily to regularity, but also to frequency and bias. B63–B30 coupling contributed to regularity and bias, and B63–B65 coupling contributed to all BMP features. Each site of plasticity altered multiple BMP features simultaneously. Moreover, plasticity loci exhibited mutual dependence and synergism. These results indicate that the memory for operant reward learning emerged from the combinatoric engagement of multiple sites of plasticity.
59.  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. ..."
60.  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.
61.  Core respiratory network organization: Insights from optogenetics and modeling (Ausborn et al 2018)
"The circuit organization within the mammalian brainstem respiratory network, specifically within and between the pre-Bötzinger (pre-BötC) and Bötzinger (BötC) complexes, and the roles of these circuits in respiratory pattern generation are continuously debated. We address these issues with a combination of optogenetic experiments and modeling studies. We used transgenic mice expressing channelrhodopsin-2 under the VGAT-promoter to investigate perturbations of respiratory circuit activity by site-specific photostimulation of inhibitory neurons within the pre-BötC or BötC. The stimulation effects were dependent on the intensity and phase of the photostimulation. Specifically: (1) Low intensity (= 1.0 mW) pulses delivered to the pre-BötC during inspiration did not terminate activity, whereas stronger stimulations (= 2.0 mW) terminated inspiration. (2) When the pre-BötC stimulation ended in or was applied during expiration, rebound activation of inspiration occurred after a fixed latency. (3) Relatively weak sustained stimulation (20 Hz, 0.5–2.0 mW) of pre-BötC inhibitory neurons increased respiratory frequency, while a further increase of stimulus intensity (> 3.0 mW) reduced frequency and finally (= 5.0 mW) terminated respiratory oscillations. (4) Single pulses (0.2–5.0 s) applied to the BötC inhibited rhythmic activity for the duration of the stimulation. (5) Sustained stimulation (20 Hz, 0.5–3.0 mW) of the BötC reduced respiratory frequency and finally led to apnea. We have revised our computational model of pre-BötC and BötC microcircuits by incorporating an additional population of post-inspiratory inhibitory neurons in the pre-BötC that interacts with other neurons in the network. This model was able to reproduce the above experimental findings as well as previously published results of optogenetic activation of pre-BötC or BötC neurons obtained by other laboratories. The proposed organization of pre-BötC and BötC circuits leads to testable predictions about their specific roles in respiratory pattern generation and provides important insights into key circuit interactions operating within brainstem respiratory networks."
62.  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. ..."
63.  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. ..."
64.  Cortico - Basal Ganglia Loop (Mulcahy et al 2020)
The model represents learning and reversal tasks and shows performance in control, Parkinsonian and Huntington disease conditions
65.  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
66.  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.
67.  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. ..."
68.  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.
69.  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
70.  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."
71.  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.
72.  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."
73.  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. ..."
74.  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. ..."
75.  Entrainment and divisive inhibition in a neocortical neural mass model (Papasavvas et al 2020)
Neural mass model of a neocortical microcircuit featuring one excitatory and two inhibitory populations. The inhibitory populations represent the soma-targeting (parvalbumin) and dendrite-targeting (somatostatin) interneurons. The model uses the Wilson-Cowan formalism and differentiates between the two inhibitory populations by the way they modulate the input-output function of the excitatory population (subtractive vs divisive inhibition, based on Wilson et al., Nature, 7411, 488, 343-348, 2012). The connectivity patterns between the populations follow the patterns reported in the primary visual cortex (Pfeffer et al., Nat Neurosci 16, 1068–1076, 2013). The model is used here to investigate the role of divisive inhibition during the entrainment of the microcircuit.
76.  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.
77.  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."
78.  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.
79.  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.
80.  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. ..."
81.  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). ..."
82.  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.
83.  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.
84.  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.
85.  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. ..."
86.  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."
87.  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.
88.  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).
89.  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).
90.  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.
91.  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.
92.  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.
93.  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.
94.  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."
95.  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.
96.  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
97.  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).
98.  Hippocampus CA1 Interneuron Specific 3 (IS3) in vivo-like virtual NN simulations (Luo et al 2020)
"Disinhibition is a widespread circuit mechanism for information selection and transfer. In the hippocampus, disinhibition of principal cells is provided by the interneuron-specific interneurons that express the vasoactive intestinal polypeptide (VIP-IS) and innervate selectively inhibitory interneurons. By combining optophysiological experiments with computational models, we determined the impact of synaptic inputs onto the network state-dependent recruitment of VIP-IS cells. We found that VIP-IS cells fire spikes in response to both the Schaffer collateral and the temporoammonic pathway activation. Moreover, by integrating their intrinsic and synaptic properties into computational models, we predicted recruitment of these cells between the rising phase and peak of theta oscillation and during ripples. Two-photon Ca2+-imaging in awake mice supported in part the theoretical predictions, revealing a significant speed modulation of VIP-IS cells and their preferential albeit delayed recruitment during theta-run epochs, with estimated firing at the rising phase and peak of the theta cycle. However, it also uncovered that VIP-IS cells are not activated during ripples. Thus, given the preferential theta-modulated firing of VIP-IS cells in awake hippocampus, we postulate that these cells may be important for information gating during spatial navigation and memory encoding."
99.  Homeostatic mechanisms may shape oscillatory modulations (Peterson & Voytek 2020)
"Neural oscillations are observed ubiquitously in the mammalian brain, but their stability is known to be rather variable. Some oscillations are tonic and last for seconds or even minutes. Other oscillations appear as unstable bursts. Likewise, some oscillations rely on excitatory AMPAergic synapses, but others are GABAergic and inhibitory. Why this diversity exists is not clear. We hypothesized Ca2+-dependent homeostasis could be important in finding an explanation. We tested this hypothesis in a highly simplified model of hippocampal neurons. In this model homeostasis profoundly alters the modulatory effect of neural oscillations. Under homeostasis, tonic AMPAergic oscillations actually decrease excitability and desynchronize firing. Tonic oscillations that are synaptically GABAergic-like those in real hippocampus-don't provoke a homeostatic response, however. If our simple model is correct, homeostasis can explain why the theta rhythm in the hippocampus is synaptically inhibitory: GABA has little to no intrinsic homeostatic response, and so can preserve the pyramidal cell's natural dynamic range. Based on these results we can also speculate that homeostasis may explain why AMPAergic oscillations in cortex, and hippocampus, often appear as bursts. Bursts do not interact with the slow homeostatic time constant, and so retain their normal excitatory effect."
100.  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."
101.  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. ..."
102.  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.
103.  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.
104.  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.
105.  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."
106.  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.
107.  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. ..."
108.  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...."
109.  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.
110.  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).
111.  Large scale neocortical model for PGENESIS (Crone et al 2019)
This is model code for a large scale neocortical model based on Traub et al. (2005), modified to run on PGENESIS on supercomputing resources. "In this paper (Crone et al 2019), we evaluate the computational performance of the GEneral NEural SImulation System (GENESIS) for large scale simulations of neural networks. While many benchmark studies have been performed for large scale simulations with leaky integrate-and-fire neurons or neuronal models with only a few compartments, this work focuses on higher fidelity neuronal models represented by 50–74 compartments per neuron. ..."
112.  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).
113.  Leech Heart Interneuron model (Sharma et al 2020)
Fractional order Leech heart interneuron model is investigated. Different firing properties are explored. In this article, we investigate the alternation of spiking and bursting phenomena of an uncoupled and coupled fractional Leech-Heart (L-H) neurons. We show that a complete graph of heterogeneous de-synchronized neurons in the backdrop of diverse memory settings (a mixture of integer and fractional exponents) can eventually lead to bursting with the formation of cluster synchronization over a certain threshold of coupling strength, however, the uncoupled L-H neurons cannot reveal bursting dynamics. Using the stability analysis in fractional domain, we demarcate the parameter space where the quiescent or steady-state emerges in uncoupled L-H neuron. Finally, a reduced-order model is introduced to capture the activities of the large network of fractional-order model neurons.
114.  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.
115.  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."
116.  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. ..."
117.  Mean-field models of neural populations under electrical stimulation (Cakan & Obermayer 2020)
Weak electrical inputs to the brain in vivo using transcranial electrical stimulation or in isolated cortex in vitro can affect the dynamics of the underlying neural populations. However, it is poorly understood what the exact mechanisms are that modulate the activity of neural populations as a whole and why the responses are so diverse in stimulation experiments. Despite this, electrical stimulation techniques are being developed for the treatment of neurological diseases in humans. To better understand these interactions, it is often necessary to simulate and analyze very large networks of neurons, which can be computationally demanding. In this theoretical paper, we present a reduced model of coupled neural populations that represents a piece of cortical tissue. This efficient model retains the dynamical properties of the large network of neurons it is based on while being several orders of magnitude faster to simulate. Due to the biophysical properties of the neuron model, an electric field can be coupled to the population. We show that weak electric fields often used in stimulation experiments can lead to entrainment of neural oscillations on the population level, and argue that the responses critically depend on the dynamical state of the neural system.
118.  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.
119.  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.
120.  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. ..."
121.  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.
122.  Midbrain dopamine neuron: firing patterns (Canavier 1999)
Sodium dynamics drives the generation of slow oscillations postulated to underly NMDA-evoked bursting activity.
123.  Minimal model of interictal and ictal discharges “Epileptor-2” (Chizhov et al 2018)
"Seizures occur in a recurrent manner with intermittent states of interictal and ictal discharges (IIDs and IDs). The transitions to and from IDs are determined by a set of processes, including synaptic interaction and ionic dynamics. Although mathematical models of separate types of epileptic discharges have been developed, modeling the transitions between states remains a challenge. A simple generic mathematical model of seizure dynamics (Epileptor) has recently been proposed by Jirsa et al. (2014); however, it is formulated in terms of abstract variables. In this paper, a minimal population-type model of IIDs and IDs is proposed that is as simple to use as the Epileptor, but the suggested model attributes physical meaning to the variables. The model is expressed in ordinary differential equations for extracellular potassium and intracellular sodium concentrations, membrane potential, and short-term synaptic depression variables. A quadratic integrate-and-fire model driven by the population input current is used to reproduce spike trains in a representative neuron. ..."
124.  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.
125.  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. ..."
126.  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.
127.  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."
128.  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."
129.  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. ..."
130.  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."
131.  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
132.  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.
133.  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.
134.  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.
135.  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.
136.  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."
137.  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.
138.  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.
139.  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. ..."
140.  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.
141.  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.
142.  Network model of movement disorders (Yousif et al 2020)
This is a Wilson-Cowan model of the basal ganglia thalamocortical cerebellar network that demonstrates healthy gamma band oscillations, Parkinsonian oscillations in the beta band and oscillations in the tremor frequency range arising from the dynamics of the network.
143.  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.
144.  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. ..."
145.  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.
146.  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
147.  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.
148.  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."
149.  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."
150.  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.
151.  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.
152.  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.
153.  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.
154.  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.
155.  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).
156.  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.
157.  Online learning model of olfactory bulb external plexiform layer network (Imam & Cleland 2020)
This model illustrates the rapid online learning of odor representations, and their recognition despite high levels of interference (other competing odorants), in a model of the olfactory bulb external plexiform layer (EPL) network. The computational principles embedded in this model are based on the those developed in the biophysical models of Li and Cleland (2013, 2017). This is a standard Python version of a model written for Intel's Loihi neuromorphic hardware platform (The Loihi code is available at https://github.com/intel-nrc-ecosystem/models/tree/master/official/epl).
158.  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.
159.  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."
160.  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."
161.  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.
162.  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."
163.  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.
164.  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.
165.  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.
166.  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.
167.  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.
168.  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). ..."
169.  Purkinje neuron network (Zang et al. 2020)
Both spike rate and timing can transmit information in the brain. Phase response curves (PRCs) quantify how a neuron transforms input to output by spike timing. PRCs exhibit strong firing-rate adaptation, but its mechanism and relevance for network output are poorly understood. Using our Purkinje cell (PC) model we demonstrate that the rate adaptation is caused by rate-dependent subthreshold membrane potentials efficiently regulating the activation of Na+ channels. Then we use a realistic PC network model to examine how rate-dependent responses synchronize spikes in the scenario of reciprocal inhibition-caused high-frequency oscillations. The changes in PRC cause oscillations and spike correlations only at high firing rates. The causal role of the PRC is confirmed using a simpler coupled oscillator network model. This mechanism enables transient oscillations between fast-spiking neurons that thereby form PC assemblies. Our work demonstrates that rate adaptation of PRCs can spatio-temporally organize the PC input to cerebellar nuclei.
170.  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.
171.  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. "
172.  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.
173.  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.
174.  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.
175.  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).
176.  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."
177.  Retinal ganglion cells responses and activity (Tsai et al 2012, Guo et al 2016)
From the abstracts: "Retinal ganglion cells (RGCs), which survive in large numbers following neurodegenerative diseases, could be stimulated with extracellular electric pulses to elicit artificial percepts. How do the RGCs respond to electrical stimulation at the sub-cellular level under different stimulus configurations, and how does this influence the whole-cell response? At the population level, why have experiments yielded conflicting evidence regarding the extent of passing axon activation? We addressed these questions through simulations of morphologically and biophysically detailed computational RGC models on high performance computing clusters. We conducted the analyses on both large-field RGCs and small-field midget RGCs. ...", "... In this study, an existing RGC ionic model was extended by including a hyperpolarization activated non-selective cationic current as well as a T-type calcium current identified in recent experimental findings. Biophysically-defined model parameters were simultaneously optimized against multiple experimental recordings from ON and OFF RGCs. ...
178.  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. "
179.  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.
180.  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."
181.  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.
182.  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.
183.  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. ..."
184.  Single neuron properties shape chaos and signal transmission in random NNs (Muscinelli et al 2019)
"While most models of randomly connected neural networks assume single-neuron models with simple dynamics, neurons in the brain exhibit complex intrinsic dynamics over multiple timescales. We analyze how the dynamical properties of single neurons and recurrent connections interact to shape the effective dynamics in large randomly connected networks. A novel dynamical mean-field theory for strongly connected networks of multi-dimensional rate neurons shows that the power spectrum of the network activity in the chaotic phase emerges from a nonlinear sharpening of the frequency response function of single neurons. For the case of two-dimensional rate neurons with strong adaptation, we find that the network exhibits a state of “resonant chaos”, characterized by robust, narrow-band stochastic oscillations. The coherence of stochastic oscillations is maximal at the onset of chaos and their correlation time scales with the adaptation timescale of single units. Surprisingly, the resonance frequency can be predicted from the properties of isolated neurons, even in the presence of heterogeneity in the adaptation parameters. In the presence of these internally-generated chaotic fluctuations, the transmission of weak, low-frequency signals is strongly enhanced by adaptation, whereas signal transmission is not influenced by adaptation in the non-chaotic regime. Our theoretical framework can be applied to other mechanisms at the level of single neurons, such as synaptic filtering, refractoriness or spike synchronization. These results advance our understanding of the interaction between the dynamics of single units and recurrent connectivity, which is a fundamental step toward the description of biologically realistic neural networks."
185.  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. "
186.  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.
187.  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."
188.  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. ..."
189.  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.
190.  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.
191.  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. ..."
192.  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
193.  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.
194.  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."
195.  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).
196.  State-dependent rhythmogenesis in a half-center locomotor CPG (Ausborn 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."
197.  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. ..."
198.  Striatal FSI and SPN oscillation model (Chartove et al. 2020)
Our model consists of three interconnected populations of single or double compartment Hodgkin-Huxley neurons: a feedforward network of FSIs, and two networks of SPNs (the D1 receptor-expressing "direct pathway" subnetwork and the D2 receptor-expressing "indirect pathway" subnetwork).
199.  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.
200.  Survey of electrically evoked responses in the retina (Tsai et al 2017)
"Cones and horizontal cells are interconnected to adjacent cones and horizontal cells, respectively, with gap junctions. In particular, the horizontal cell gap junctional conductance is modulated by exogenous factors. What roles does this conductance play in the electrically evoked responses of horizontal cells? To address this question, we constructed a computational model consisting of the cone and horizontal cell layer..."
201.  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.
202.  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. ..."
203.  Synaptic strengths are critical in creating the proper output phasing in a CPG (Gunay et al 2019)
"Identified neurons and the networks they compose produce stereotypical, albeit individually unique, activity across members of a species. We propose, for a motor circuit driven by a central pattern generator (CPG), that the uniqueness derives mainly from differences in synaptic strength rather than from differences in intrinsic membrane conductances. We studied a dataset of recordings from six leech (Hirudo sp.) heartbeat control networks, containing complete spiking activity patterns from inhibitory premotor interneurons, motor output spike patterns, and synaptic strength patterns to investigate the source of uniqueness. We used a conductance-based multicompartmental motor neuron model to construct a bilateral motor circuit model, and controlled it by playing recorded input spike trains from premotor interneurons to generate output inhibitory synaptic patterns similar to experimental measurements. By generating different synaptic conductance parameter sets of this circuit model, we found that relative premotor synaptic strengths impinging onto motor neurons must be different across individuals to produce animal-specific output burst phasing. Obtaining unique outputs from each individual’s circuit model did not require different intrinsic ionic conductance parameters. Furthermore, changing intrinsic conductances failed to compensate for modified synaptic strength patterns. ..."
204.  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. ..."
205.  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.
206.  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."
207.  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.
208.  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.
209.  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
210.  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).
211.  Thalamic Reticular Network (Destexhe et al 1994)
Demo for simulating networks of thalamic reticular neurons (reproduces figures from Destexhe A et al 1994)
212.  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)
213.  Thalamocortical control of propofol phase-amplitude coupling (Soplata et al 2017)
"The anesthetic propofol elicits many different spectral properties on the EEG, including alpha oscillations (8-12 Hz), Slow Wave Oscillations (SWO, 0.1-1.5 Hz), and dose-dependent phase-amplitude coupling (PAC) between alpha and SWO. Propofol is known to increase GABAA inhibition and decrease H-current strength, but how it generates these rhythms and their interactions is still unknown. To investigate both generation of the alpha rhythm and its PAC to SWO, we simulate a Hodgkin-Huxley network model of a hyperpolarized thalamus and corticothalamic inputs. ..."
214.  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.
215.  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.
216.  The APP in C-terminal domain alters CA1 neuron firing (Pousinha et al 2019)
"The amyloid precursor protein (APP) is central to AD pathogenesis and we recently showed that its intracellular domain (AICD) could modify synaptic signal integration. We now hypothezise that AICD modifies neuron firing activity, thus contributing to the disruption of memory processes. Using cellular, electrophysiological and behavioural techniques, we showed that pathological AICD levels weakens CA1 neuron firing activity through a gene transcription-dependent mechanism. Furthermore, increased AICD production in hippocampal neurons modifies oscillatory activity, specifically in the gamma frequency range, and disrupts spatial memory task. Collectively, our data suggest that AICD pathological levels, observed in AD mouse models and in human patients, might contribute to progressive neuron homeostatic failure, driving the shift from normal ageing to AD."
217.  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."
218.  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. ..."
219.  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."
220.  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.
221.  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. ..."
222.  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. ..."
223.  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.

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