Models that contain the Receptor : AMPA

Re-display model names without descriptions

	Models	Description
1.	2D model of olfactory bulb gamma oscillations (Li and Cleland 2017)
		This is a biophysical model of the olfactory bulb (OB) that contains three types of neurons: mitral cells, granule cells and periglomerular cells. The model is used to study the cellular and synaptic mechanisms of OB gamma oscillations. We concluded that OB gamma oscillations can be best modeled by the coupled oscillator architecture termed pyramidal resonance inhibition network gamma (PRING).
2.	3D olfactory bulb: operators (Migliore et al, 2015)
		"... Using a 3D model of mitral and granule cell interactions supported by experimental findings, combined with a matrix-based representation of glomerular operations, we identify the mechanisms for forming one or more glomerular units in response to a given odor, how and to what extent the glomerular units interfere or interact with each other during learning, their computational role within the olfactory bulb microcircuit, and how their actions can be formalized into a theoretical framework in which the olfactory bulb can be considered to contain "odor operators" unique to each individual. ..."
3.	A 1000 cell network model for Lateral Amygdala (Kim et al. 2013)
		1000 Cell Lateral Amygdala model for investigation of plasticity and memory storage during Pavlovian Conditioning.
4.	A Computational Model of Bidirectional Plasticity Regulation by betaCaMKII (Pinto et al. 2019)
		We present a computational model that suggests how calcium-calmodulin dependent protein kinase II can act as a molecular switch in synaptic plasticity induction at an important cerebellar synapse (between parallel fibres and Purkinje cells). Our simulation results provide a potential explanation for experimental data by van Woerden et al (Van Woerden G, Hoebeek F, Gao Z, Nagaraja R, Hoogenraad C, Kushner S, et al. [beta]CaMKII controls the direction of plasticity at parallel fiber-Purkinje cell synapses. Nat Neurosci. 2009;12(7):823-825). These experiments were performed in the lab led by Professor Chris De Zeeuw.
5.	A computational model of systems memory consolidation and reconsolidation (Helfer & Shultz 2019)
		A neural-network framework for modeling systems memory consolidation and reconsolidation.
6.	A detailed data-driven network model of prefrontal cortex (Hass et al 2016)
		Data-based PFC-like circuit with layer 2/3 and 5, synaptic clustering, four types of interneurons and cell-type specific short-term synaptic plasticity; neuron parameters fitted to in vitro data, all other parameters constrained by experimental literature. Reproduces key features of in vivo resting state activity without specific tuning.
7.	A focal seizure model with ion concentration changes (Gentiletti et al., 2022)
		Computer model was used to investigate the possible mechanisms of seizure initiation, progression and termination. The model was developed by complementing the Hodgkin-Huxley equations with activity-dependent changes in intra- and extracellular ion concentrations. The model incorporates a number of ionic mechanisms such as: active and passive membrane currents, inhibitory synaptic GABAA currents, Na/K pump, KCC2 cotransporter, glial K buffering, radial diffusion between extracellular space and bath, and longitudinal diffusion between dendritic and somatic compartments in pyramidal cells.
8.	A kinetic model unifying presynaptic short-term facilitation and depression (Lee et al. 2009)
		"... Here, we propose a unified theory of synaptic short-term plasticity based on realistic yet tractable and testable model descriptions of the underlying intracellular biochemical processes. Analysis of the model equations leads to a closed-form solution of the resonance frequency, a function of several critical biophysical parameters, as the single key indicator of the propensity for synaptic facilitation or depression under repetitive stimuli. This integrative model is supported by a broad range of transient and frequency response experimental data including those from facilitating, depressing or mixed-mode synapses. ... the model provides the reasons behind the switching behavior between facilitation and depression observed in experiments. ..."
9.	A Method for Prediction of Receptor Activation in the Simulation of Synapses (Montes et al. 2013)
		A machine-learning based method that can accurately predict relevant aspects of the behavior of synapses, such as the activation of synaptic receptors, at very low computational cost. The method is designed to learn patterns and general principles from previous Monte Carlo simulations and to predict synapse behavior from them. The resulting procedure is accurate, automatic and can predict synapse behavior under experimental conditions that are different to the ones used during the learning phase. Since our method efficiently reduces the computational costs, it is suitable for the simulation of the vast number of synapses that occur in the mammalian brain.
10.	A Model Circuit of Thalamocortical Convergence (Behuret et al. 2013)
		“… Using dynamic-clamp techniques in thalamic slices in vitro, we combined theoretical and experimental approaches to implement a realistic hybrid retino-thalamo-cortical pathway mixing biological cells and simulated circuits. … The study of the impact of the simulated cortical input on the global retinocortical signal transfer efficiency revealed a novel control mechanism resulting from the collective resonance of all thalamic relay neurons. We show here that the transfer efficiency of sensory input transmission depends on three key features: i) the number of thalamocortical cells involved in the many-to-one convergence from thalamus to cortex, ii) the statistics of the corticothalamic synaptic bombardment and iii) the level of correlation imposed between converging thalamic relay cells. In particular, our results demonstrate counterintuitively that the retinocortical signal transfer efficiency increases when the level of correlation across thalamic cells decreases. …”
11.	A model for interaural time difference sensitivity in the medial superior olive (Zhou et al 2005)
		This model simulates responses of neurons to interaural time difference (ITD) in the medial superior olive (MSO) of the mammalian brainstem. The model has a bipolar cell structure and incorporates two anatomic observations in the MSO: (1) the axon arises from the dendrite that receives ipsilateral inputs and (2) inhibitory synapses are located primarily on the soma in adult animals. Fine adjustment of the best ITD is achieved by the interplay of somatic sodium currents and synaptic inhibitory currents. The model suggests a mechanism for dynamically "fine-tuning" the ITD sensitivity of MSO cells by the opponency between depolarizing sodium currents and hyperpolarizing inhibitory currents.
12.	A model of cerebellar LTD including RKIP inactivation of Raf and MEK (Hepburn et al 2017)
		An updated stochastic model of cerebellar Long Term Depression (LTD) with improved realism. Dissociation of Raf kinase inhibitor protein (RKIP) from Mitogen-activated protein kinase kinase (MEK) and Raf kinase are added to an earlier published model. Calcium dynamics is updated as a constant-rate influx to more closely match experiment. AMPA receptor interactions are improved by adding phosphorylation and dephosphorylation of AMPA receptors when bound to glutamate receptor interacting protein (GRIP). The model is tuned to reproduce experimental calcium peak vs LTD amplitude curves accurately at 4 different calcium pulse durations.
13.	A model of unitary responses from A/C and PP synapses in CA3 pyramidal cells (Baker et al. 2010)
		The model was used to reproduce experimentally determined mean synaptic response characteristics of unitary AMPA and NMDA synaptic stimulations in CA3 pyramidal cells with the objective of inferring the most likely response properties of the corresponding types of synapses. The model is primarily concerned with passive cells, but models of active dendrites are included.
14.	A multilayer cortical model to study seizure propagation across microdomains (Basu et al. 2015)
		A realistic neural network was used to simulate a region of neocortex to obtain extracellular LFPs from ‘virtual micro-electrodes’ and produce test data for comparison with multisite microelectrode recordings. A model was implemented in the GENESIS neurosimulator. A simulated region of cortex was represented by layers 2/3, 5/6 (interneurons and pyramidal cells) and layer 4 stelate cells, spaced at 25 µm in each horizontal direction. Pyramidal cells received AMPA and NMDA inputs from neighboring cells at the basal and apical dendrites. The LFP data was generated by simulating 16-site electrode array with the help of ‘efield’ objects arranged at the predetermined positions with respect to the surface of the simulated network. The LFP for the model is derived from a weighted average of the current sources summed over all cellular compartments. Cell models were taken from from Traub et al. (2005) J Neurophysiol 93(4):2194-232.
15.	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."
16.	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."
17.	A network model of tail withdrawal in Aplysia (White et al 1993)
		The contributions of monosynaptic and polysynaptic circuitry to the tail-withdrawal reflex in the marine mollusk Aplysia californica were assessed by the use of physiologically based neural network models. Effects of monosynaptic circuitry were examined by the use of a two-layer network model with four sensory neurons in the input layer and one motor neuron in the output layer. Results of these simulations indicated that the monosynaptic circuit could not account fully for long-duration responses of tail motor neurons elicited by tail stimulation. A three-layer network model was constructed by interposing a layer of two excitatory interneurons between the input and output layers of the two-layer network model. The three-layer model could account for long-duration responses in motor neurons. Sensory neurons are a known site of plasticity in Aplysia. Synaptic plasticity at more than one locus modified dramatically the input-output relationship of the three-layer network model. This feature gave the model redundancy in its plastic properties and points to the possibility of distributed memory in the circuitry mediating withdrawal reflexes in Aplysia. Please see paper for more results and details.
18.	A neuronal circuit simulator for non Monte Carlo analysis of neuronal noise (Kilinc & Demir 2018)
		cirsiumNeuron is a neuronal circuit simulator that can directly and efficiently compute characterizations of stochastic behavior, i.e., noise, for multi-neuron circuits. In cirsiumNeuron, we utilize a general modeling framework for biological neuronal circuits which systematically captures the nonstationary stochastic behavior of the ion channels and the synaptic processes. In this framework, we employ fine-grained, discrete-state, continuous-time Markov Chain (MC) models of both ion channels and synaptic processes in a unified manner. Our modeling framework can automatically generate the corresponding coarse-grained, continuous-state, continuous-time Stochastic Differential Equation (SDE) models. In addition, for the stochastic characterization of neuronal variability and noise, we have implemented semi-analytical, non Monte Carlo analysis techniques that work both in time and frequency domains, which were previously developed for analog electronic circuits. In these semi-analytical noise evaluation schemes, (differential) equations that directly govern probabilistic characterizations in the form of correlation functions (time domain) or spectral densities (frequency domain) are first derived analytically, and then solved numerically. These semi-analytical noise analysis techniques correctly and accurately capture the second order statistics (mean, variance, autocorrelation, and power spectral density) of the underlying neuronal processes as compared with Monte Carlo simulations.
19.	A sensorimotor-spinal cord model (Hoshino et al. 2022)
		To elucidate how the flattening of sensory tuning due to a deficit in tonic inhibition slows motor responses, we simulated a neural network model in which a sensory cortical network (NS) and a motor cortical network (NM) are reciprocally connected, and the NM projects to spinal motoneurons (Mns). The NS was presented with a feature stimulus and the reaction time of Mns was measured. The flattening of sensory tuning in NS caused by decreasing the centration of GABA in extracellular space resulted in a decrease in the stimulus-sensitive NM pyramidal cell activity while increasing the stimulus-insensitive NM pyramidal cell activity, thereby prolonging the reaction time of Mns to the applied feature stimulus. We suggest that a reduction in extracellular GABA concentration in sensory cortex may interfere with selective activation in motor cortex, leading to slowing the activation of spinal motoneurons and therefore to slowing motor responses.
20.	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.
21.	A synapse model for developing somatosensory cortex (Manninen et al 2020)
		We developed a model for an L4-L2/3 synapse in somatosensory cortex to study the role of astrocytes in modulation of t-LTD. Our model includes the one-compartmental presynaptic L4 spiny stellate cell, two-compartmental (soma and dendrite) postsynaptic L2/3 pyramidal cell, and one-compartmental fine astrocyte process.
22.	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.
23.	A two-layer biophysical olfactory bulb model of cholinergic neuromodulation (Li and Cleland 2013)
		This is a two-layer biophysical olfactory bulb (OB) network model to study cholinergic neuromodulation. Simulations show that nicotinic receptor activation sharpens mitral cell receptive field, while muscarinic receptor activation enhances network synchrony and gamma oscillations. This general model suggests that the roles of nicotinic and muscarinic receptors in OB are both distinct and complementary to one another, together regulating the effects of ascending cholinergic inputs on olfactory bulb transformations.
24.	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.
25.	Acetylcholine Boosts Dendritic NMDA Spikes in a CA3 Pyramidal Neuron Model (Humphries et al., 2021)
		This model was used to compare the nonlinearity of NMDA inputs between dendritic sections in a CA3 pyramidal neuron as well as investigate the effect of cholinergic modulation/potassium channel inhibition on this dendritic NMDA-mediated nonlinearity.
26.	ACnet23 primary auditory cortex model (Beeman et al 2019)
		These scripts were used to model a patch of layer 2/3 primary auditory cortex, making use of the the improvements to PGENESIS by Crone, et al. (2019). This single layer model contains a 48 x 48 grid of pyramidal cells (PCs) and a 24 x 24 grid of basket cells (BCs). The reduced PC models have 17 compartments with dimensions and passive properties that were fit to human cortical PC reconstructions. This parallel version of the simulation was used by Beeman, et al. (2019) to understand the effects of inhibition of PCs by BCs on auditory evoked potentials.
27.	Active dendrites and spike propagation in a hippocampal interneuron (Saraga et al 2003)
		We create multi-compartment models of an Oriens-Lacunosum/Moleculare (O-LM) hippocampal interneuron using passive properties, channel kinetics, densities and distributions specific to this cell type, and explore its signaling characteristics. We find that spike initiation depends on both location and amount of input, as well as the intrinsic properties of the interneuron. Distal synaptic input always produces strong back-propagating spikes whereas proximal input could produce both forward and back-propagating spikes depending on the input strength. Please see paper for more details.
28.	Active dendrites shape signaling microdomains in hippocampal neurons (Basak & Narayanan 2018)
		The spatiotemporal spread of biochemical signals in neurons and other cells regulate signaling specificity, tuning of signal propagation, along with specificity and clustering of adaptive plasticity. Theoretical and experimental studies have demonstrated a critical role for cellular morphology and the topology of signaling networks in regulating this spread. In this study, we add a significantly complex dimension to this narrative by demonstrating that voltage-gated ion channels (A-type Potassium channels and T-type Calcium channels) on the plasma membrane could actively amplify or suppress the strength and spread of downstream signaling components. We employed a multiscale, multicompartmental, morphologically realistic, conductance-based model that accounted for the biophysics of electrical signaling and the biochemistry of calcium handling and downstream enzymatic signaling in a hippocampal pyramidal neuron. We chose the calcium – calmodulin – calcium/calmodulin-dependent protein kinase II (CaMKII) – protein phosphatase 1 (PP1) signaling pathway owing to its critical importance to several forms of neuronal plasticity, and employed physiologically relevant theta-burst stimulation (TBS) or theta-burst pairing (TBP) protocol to initiate a calcium microdomain through NMDAR activation at a synapse.
29.	Active dendritic integration in robust and precise grid cell firing (Schmidt-Hieber et al 2017)
		"... Whether active dendrites contribute to the generation of the dual temporal and rate codes characteristic of grid cell output is unknown. We show that dendrites of medial entorhinal cortex neurons are highly excitable and exhibit a supralinear input–output function in vitro, while in vivo recordings reveal membrane potential signatures consistent with recruitment of active dendritic conductances. By incorporating these nonlinear dynamics into grid cell models, we show that they can sharpen the precision of the temporal code and enhance the robustness of the rate code, thereby supporting a stable, accurate representation of space under varying environmental conditions. Our results suggest that active dendrites may therefore constitute a key cellular mechanism for ensuring reliable spatial navigation."
30.	Activity dependent changes in dendritic spine density and spine structure (Crook et al. 2007)
		"... In this work, we extend previous modeling studies [27] by combining a model for activity-dependent spine density with one for calcium-mediated spine stem restructuring. ... Additional equations characterize the change in spine density along the dendrite, the current balance equation for an individual spine head, the change in calcium concentration in the spine head, and the dynamics of spine stem resistance. We use computational studies to investigate the changes in spine density and structure for differing synaptic inputs and demonstrate the effects of these changes on the input-output properties of the dendritic branch. ... "
31.	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."
32.	Ambient glutamate shapes AMPA receptor responses to simulated transients (Balmer et al. 2021)
		To explore how ambient glutamate contributes to the generation of ultra-slow signaling through AMPARs at the cerebellar unipolar brush cell synapse, we created this 13-state kinetic model in NEURON. A tool was also created to produce trains of glutamate concentration transients using 2D or 3D diffusion equations, a sum of up to 3 exponentials, or an alpha function that can be applied to the AMPA receptor model. After compiling the model using mkrndll, run 'mosinit_fast-flow.hoc' to simulate fast application of glutamate to the AMPA receptor model. 'mosinit_GluTransTrainTool_demo.hoc' opens a session where trains of synaptic glutamate transients can be created using various equations. The top panel shows the glutamate concentration transients (in mM) and the bottom panel shows the AMPA receptor mediated currents (in nA).
33.	AMPA receptor trafficking and its role in heterosynaptic plasticity (Antunes et al 2018)
		"... cumulative experimental and theoretical data have demonstrated that long-term potentiation (LTP) and long-term depression (LTD) can promote compensatory alterations in non-stimulated synapses. In this work, we have developed a (MCELL) computational model of a (3D) spiny dendritic segment to investigate the role of AMPA receptor (AMPAR) trafficking during synaptic plasticity at specific synapses and its consequences for the populations of AMPAR at nearby synapses. Our results demonstrated that the mechanisms of AMPAR trafficking involved with LTP and LTD can promote heterosynaptic plasticity at non-stimulated synapses. These alterations are compensatory and arise from molecular competition. Moreover, the heterosynaptic changes observed in our model can modulate further activity-driven inductions of synaptic plasticity." The model requires an installed version of MCell and CellBlender.
34.	Amyloid-beta effects on release probability and integration at CA3-CA1 synapses (Romani et al. 2013)
		The role of amyloid beta (Aß) in brain function and in the pathogenesis of Alzheimer’s disease remains elusive. Recent publications reported that an increase in Aß concentration perturbs presynaptic release in hippocampal neurons, in particular by increasing release probability of CA3-CA1 synapses. The model predics how this alteration can affect synaptic plasticity and signal integration. The results suggest that the perturbation of release probability induced by increased Aß can significantly alter the spike probability of CA1 pyramidal neurons and thus contribute to abnormal hippocampal function during Alzheimer’s disease.
35.	An allosteric kinetics of NMDARs in STDP (Urakubo et al. 2008)
		"... We developed a detailed biophysical model of STDP and found that the model required spike timing-dependent distinct suppression of NMDARs by Ca2+-calmodulin. This led us to predict an allosteric kinetics of NMDARs: a slow and rapid suppression of NMDARs by Ca2+-calmodulin with prespiking -> postspiking and postspiking -> prespiking, respectively. We found that the allosteric kinetics, but not the conventional kinetics, is consistent with specific features of amplitudes and peak time of NMDAR-mediated EPSPs in experiments. ..." See paper for more and details.
36.	Analytical modelling of temperature effects on an AMPA-type synapse (Kufel & Wojcik 2018)
		This code was used in the construction of the model developed in the paper. It is a modified version of the simulation developed by Postlethwaite et al. 2007 - for details of modifications refer to the main body of Kufel & Wojcik (2018).
37.	Application of a common kinetic formalism for synaptic models (Destexhe et al 1994)
		Application to AMPA, NMDA, GABAA, and GABAB receptors is given in a book chapter. The reference paper synthesizes a comprehensive general description of synaptic transmission with Markov kinetic models. This framework is applicable to modeling ion channels, synaptic release, and all receptors. Please see the references for more details. A simple introduction to this method is given in a seperate paper Destexhe et al Neural Comput 6:14-18 , 1994). More information and papers at http://cns.iaf.cnrs-gif.fr/Main.html and through email: Destexhe@iaf.cnrs-gif.fr
38.	Ave. neuron model for slow-wave sleep in cortex Tatsuki 2016 Yoshida 2018 Rasmussen 2017 (all et al)
		Averaged neuron(AN) model is a conductance-based (Hodgkin-Huxley type) neuron model which includes a mean-field approximation of a population of neurons. You can simulate previous models (AN model: Tatsuki et al., 2016 and SAN model: Yoshida et al., 2018), and various models with 'X model' based on channel and parameter modules. Also, intracellular and extracellular ion concentration can be taken into consideration using the Nernst equation (See Ramussen et al., 2017).
39.	Axonal subthreshold voltage signaling along hippocampal mossy fiber (Kamiya 2022)
		Subthreshold depolarization of soma passively propagates into the axons for a substantial distance and thereby caused enhancement of the transmitter release from the axon terminals of hippocampal mossy fibers. Here we developed the granule cell-mossy fiber model implemented with axonal sodium potassium and calcium channels and explored the mechanisms underlying analog modulation of the action potential-evoked transmitter release by subthreshold voltage signaling along the axons. Action potential-induced calcium entry to the terminals was reduced, while subthreshold depolarization itself caused small calcium entry.
40.	BCM-like synaptic plasticity with conductance-based models (Narayanan Johnston, 2010)
		" ... Although the BCM-like plasticity framework has been a useful formulation to understand synaptic plasticity and metaplasticity, a mechanism for the activity-dependent regulation of this modification threshold has remained an open question. In this simulation study based on CA1 pyramidal cells, we use a modification of the calcium-dependent hypothesis proposed elsewhere and show that a change in the hyperpolarization-activated, nonspecific-cation h current is capable of shifting the modification threshold. ..."
41.	Biologically Constrained Basal Ganglia model (BCBG model) (Lienard, Girard 2014)
		We studied the physiology and function of the basal ganglia through the design of mean-field models of the whole basal ganglia. The parameterizations are optimized with multi-objective evolutionary algorithm to respect best a collection of numerous anatomical data and electrophysiological data. The main outcomes of our study are: • The strength of the GPe to GPi/SNr connection does not support opposed activities in the GPe and GPi/SNr. • STN and MSN target more the GPe than the GPi/SNr. • Selection arises from the structure of the basal ganglia, without properly segregated direct and indirect pathways and without specific inputs from pyramidal tract neurons of the cortex. Selection is enhanced when the projection from GPe to GPi/SNr has a diffuse pattern.
42.	Biophysical and phenomenological models of spike-timing dependent plasticity (Badoual et al. 2006)
		"Spike-timing dependent plasticity (STDP) is a form of associative synaptic modification which depends on the respective timing of pre- and post-synaptic spikes. The biophysical mechanisms underlying this form of plasticity are currently not known. We present here a biophysical model which captures the characteristics of STDP, such as its frequency dependency, and the effects of spike pair or spike triplet interactions. ... A simplified phenomenological model is also derived..."
43.	Biophysical modeling of pathological brain states (Sudhakar et al 2019)
		"Traumatic brain injuries (TBI) lead to dramatic changes in the surviving brain tissue. Altered ion concentrations, coupled with changes in the expression of membrane-spanning proteins, create a post-TBI brain state that can lead to further neuronal loss caused by secondary excitotoxicity. Several GABA receptor agonists have been tested in the search for neuroprotection immediately after an injury, with paradoxical results. These drugs not only fail to offer neuroprotection, but can also slow down functional recovery after TBI. Here, using computational modeling, we provide a biophysical hypothesis to explain these observations. We show that the accumulation of intracellular chloride ions caused by a transient upregulation of Na+-K+-2Cl- (NKCC1) co-transporters as observed following TBI, causes GABA receptor agonists to lead to excitation and depolarization block, rather than the expected hyperpolarization. The likelihood of prolonged, excitotoxic depolarization block is further exacerbated by the extremely high levels of extracellular potassium seen after TBI. Our modeling results predict that the neuroprotective efficacy of GABA receptor agonists can be substantially enhanced when they are combined with NKCC1 co-transporter inhibitors. This suggests a rational, biophysically principled method for identifying drug combinations for neuroprotection after TBI."
44.	Biophysically realistic neural modeling of the MEG mu rhythm (Jones et al. 2009)
		"Variations in cortical oscillations in the alpha (7–14 Hz) and beta (15–29 Hz) range have been correlated with attention, working memory, and stimulus detection. The mu rhythm recorded with magnetoencephalography (MEG) is a prominent oscillation generated by Rolandic cortex containing alpha and beta bands. Despite its prominence, the neural mechanisms regulating mu are unknown. We characterized the ongoing MEG mu rhythm from a localized source in the finger representation of primary somatosensory (SI) cortex. Subjects showed variation in the relative expression of mu-alpha or mu-beta, which were nonoverlapping for roughly 50% of their respective durations on single trials. To delineate the origins of this rhythm, a biophysically principled computational neural model of SI was developed, with distinct laminae, inhibitory and excitatory neurons, and feedforward (FF, representative of lemniscal thalamic drive) and feedback (FB, representative of higher-order cortical drive or input from nonlemniscal thalamic nuclei) inputs defined by the laminar location of their postsynaptic effects. ..."
45.	Burst induced synaptic plasticity in Apysia sensorimotor neurons (Phares et al 2003)
		The Aplysia sensorimotor synapse is a key site of plasticity for several simple forms of learning. Intracellular stimulation of sensory neurons to fire a burst of action potentials at 10 Hz for 1 sec led to significant homosynaptic depression of postsynaptic responses. During the burst, the steady-state depressed phase of the postsynaptic response, which was only 20% of the initial EPSP of the burst, still contributed to firing the motor neuron. To explore the functional contribution of transient homosynaptic depression to the response of the motor neuron, computer simulations of the sensorimotor synapse with and without depression were compared. Depression allowed the motor neuron to produce graded responses over a wide range of presynaptic input strength. Thus, synaptic depression increased the dynamic range of the sensorimotor synapse and can, in principle, have a profound effect on information processing. Please see paper for results and details.
46.	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. ..."
47.	CA1 network model for place cell dynamics (Turi et al 2019)
		Biophysical model of CA1 hippocampal region. The model simulates place cells/fields and explores the place cell dynamics as function of VIP+ interneurons.
48.	CA1 network model: interneuron contributions to epileptic deficits (Shuman et al 2020)
		Temporal lobe epilepsy causes significant cognitive deficits in both humans and rodents, yet the specific circuit mechanisms underlying these deficits remain unknown. There are profound and selective interneuron death and axonal reorganization within the hippocampus of both humans and animal models of temporal lobe epilepsy. To assess the specific contribution of these mechanisms on spatial coding, we developed a biophysically constrained network model of the CA1 region that consists of different subtypes of interneurons. More specifically, our network consists of 150 cells, 130 excitatory pyramidal cells and 20 interneurons (Fig. 1A). To simulate place cell formation in the network model, we generated grid cell and place cell inputs from the Entorhinal Cortex (ECLIII) and CA3 regions, respectively, activated in a realistic manner as observed when an animal transverses a linear track. Realistic place fields emerged in a subpopulation of pyramidal cells (40-50%), in which similar EC and CA3 grid cell inputs converged onto distal/proximal apical and basal dendrites. The tuning properties of these cells are very similar to the ones observed experimentally in awake, behaving animals To examine the role of interneuron death and axonal reorganization in the formation and/or tuning properties of place fields we selectively varied the contribution of each interneuron type and desynchronized the two excitatory inputs. We found that desynchronized inputs were critical in reproducing the experimental data, namely the profound reduction in place cell numbers, stability and information content. These results demonstrate that the desynchronized firing of hippocampal neuronal populations contributes to poor spatial processing in epileptic mice, during behavior. Given the lack of experimental data on the selective contributions of interneuron death and axonal reorganization in spatial memory, our model findings predict the mechanistic effects of these alterations at the cellular and network levels.
49.	CA1 pyr cell: phenomenological NMDAR-based model of synaptic plasticity (Dainauskas et al 2023)
		This Python code implements a phenomenological NMDA receptor-based voltage-dependent model of synaptic plasticity for CA3-CA1 synapse and shows weight changes of a synapse placed on a two-compartmental model of a hippocampal CA1 pyramidal neuron for spike-timing-dependent synaptic plasticity (STDP) and frequency-dependent synaptic plasticity stimulation protocols. The developed model predicts altered learning rules in synapses formed on the apical dendrites of the detailed compartmental model of CA1 pyramidal neuron in the presence of the GluN2B-NMDA receptor hypofunction.
50.	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.
51.	CA1 pyramidal neuron dendritic spine with plasticity (O`Donnell et al. 2011)
		Biophysical model of a dendritic spine and adjacent dendrite with synapse. Model parameters adjusted to fit CA3-CA1 Shaffer collateral synapse data from literature. Model includes both electrical and Ca2+ dynamics, including AMPARs, NMDARs, 4 types of CaV channel, and leak conductance. Spine and synapse are plastic according to Ca2+ dependent rule. The aim of the model is to explore the effects of dendritic spine structural plasticity on the rules of synaptic plasticity.
52.	CA1 pyramidal neuron synaptic integration (Bloss et al. 2016)
		"... We examined synaptic connectivity between molecularly defined inhibitory interneurons and CA1 pyramidal cell dendrites using correlative light-electron microscopy and large-volume array tomography. We show that interneurons can be highly selective in their connectivity to specific dendritic branch types and, furthermore, exhibit precisely targeted connectivity to the origin or end of individual branches. Computational simulations indicate that the observed subcellular targeting enables control over the nonlinear integration of synaptic input or the initiation and backpropagation of action potentials in a branchselective manner. Our results demonstrate that connectivity between interneurons and pyramidal cell dendrites is more precise and spatially segregated than previously appreciated, which may be a critical determinant of how inhibition shapes dendritic computation."
53.	CA1 pyramidal neuron synaptic integration (Li and Ascoli 2006, 2008)
		The model shows how different input patterns (irregular & asynchronous, irregular & synchronous, regular & asynchronous, regular & synchronous) affect the neuron's output rate when 1000 synapses are distributed in the proximal apical dendritic tree of a hippocampus CA1 pyramidal neuron.
54.	CA1 pyramidal neuron: action potential backpropagation (Gasparini & Migliore 2015)
		" ... the investigation of AP backpropagation and its functional roles has greatly benefitted from computational models that use biophysically and morphologically accurate implementations. ..." This model entry recreates figures 2 and 4 from the paper illustrating how conductance densities of voltage gated channels (fig 2) and the timing of synaptic input with backpropagating action potentials (fig 4) affects membrane voltage trajectories.
55.	CA1 pyramidal neuron: calculation of MRI signals (Cassara et al. 2008)
		NEURON mod files from the paper: Cassarà AM, Hagberg GE, Bianciardi M, Migliore M, Maraviglia B. Realistic simulations of neuronal activity: A contribution to the debate on direct detection of neuronal currents by MRI. Neuroimage. 39:87-106 (2008). In this paper, we use a detailed calculation of the magnetic field produced by the neuronal currents propagating over a hippocampal CA1 pyramidal neuron placed inside a cubic MR voxel of length 1.2 mm to estimate the Magnetic Resonance signal.
56.	CA1 pyramidal neuron: conditional boosting of dendritic APs (Watanabe et al 2002)
		Model files from the paper Watanabe S, Hoffman DA, Migliore M, Johnston D (2002). The experimental and modeling results support the hypothesis that dendritic K-A channels and the boosting of back-propagating action potentials contribute to the induction of LTP in CA1 neurons. See the paper for details. Questions about the model may be addressed to Michele Migliore: michele.migliore@pa.ibf.cnr.it
57.	CA1 pyramidal neuron: Dendritic Na+ spikes are required for LTP at distal synapses (Kim et al 2015)
		This model simulates the effects of dendritic sodium spikes initiated in distal apical dendrites on the voltage and the calcium dynamics revealed by calcium imaging. It shows that dendritic sodium spike promotes large and transient calcium influxes via NMDA receptor and L-type voltage-gated calcium channels, which contribute to the induction of LTP at distal synapses.
58.	CA1 pyramidal neuron: dendritic spike initiation (Gasparini et al 2004)
		NEURON mod files from the paper: Sonia Gasparini, Michele Migliore, and Jeffrey C. Magee On the initiation and propagation of dendritic spikes in CA1 pyramidal neurons, J. Neurosci., J. Neurosci. 24:11046-11056 (2004).
59.	CA1 pyramidal neuron: depolarization block (Bianchi et al. 2012)
		NEURON files from the paper: On the mechanisms underlying the depolarization block in the spiking dynamics of CA1 pyramidal neurons by D.Bianchi, A. Marasco, A.Limongiello, C.Marchetti, H.Marie,B.Tirozzi, M.Migliore (2012). J Comput. Neurosci. In press. DOI: 10.1007/s10827-012-0383-y. Experimental findings shown that under sustained input current of increasing strength neurons eventually stop firing, entering a depolarization block. We analyze the spiking dynamics of CA1 pyramidal neuron models using the same set of ionic currents on both an accurate morphological reconstruction and on its reduction to a single-compartment. The results show the specic ion channel properties and kinetics that are needed to reproduce the experimental findings, and how their interplay can drastically modulate the neuronal dynamics and the input current range leading to depolarization block.
60.	CA1 pyramidal neuron: effects of Ih on distal inputs (Migliore et al 2004)
		NEURON mod files from the paper: M. Migliore, L. Messineo, M. Ferrante Dendritic Ih selectively blocks temporal summation of unsynchronized distal inputs in CA1 pyramidal neurons, J.Comput. Neurosci. 16:5-13 (2004). The model demonstrates how the dendritic Ih in pyramidal neurons could selectively suppress AP generation for a volley of excitatory afferents when they are asynchronously and distally activated.
61.	CA1 pyramidal neuron: effects of Lamotrigine on dendritic excitability (Poolos et al 2002)
		NEURON mod files from N. Poolos, M. Migliore, and D. Johnston, Nature Neuroscience (2002). The experimental and modeling results in this paper demonstrate for the first time that neuronal excitability can be altered by pharmaceuticals acting selectively on dendrites, and suggest an important role for Ih in controlling dendritic excitability and epileptogenesis.
62.	CA1 pyramidal neuron: integration of subthreshold inputs from PP and SC (Migliore 2003)
		The model shows how the experimentally observed increase in the dendritic density of Ih and IA could have a major role in constraining the temporal integration window for the main CA1 synaptic inputs.
63.	CA1 pyramidal neuron: nonlinear a5-GABAAR controls synaptic NMDAR activation (Schulz et al 2018)
		The study shows that IPSCs mediated by a5-subunit containing GABAA receptors are strongly outward-rectifying generating 4-fold larger conductances above -50?mV than at rest. Experiments and modeling show that synaptic activation of these receptors can very effectively control voltage-dependent NMDA-receptor activation in a spatiotemporally controlled manner in fine dendrites of CA1 pyramidal cells. The files contain the NEURON code for Fig.8, Fig.S8 and Fig.S9 of the paper. The model is based on the model published by Bloss et al., 2017. Physiological properties of GABA synapses were modified as determined by optogenetic activation of inputs during voltage-clamp recordings in Schulz et al. 2018. Other changes include stochastic synaptic release and short-term synaptic plasticity. All changes of mechanisms and parameters are detailed in the Methods of the paper. Simulation can be run by starting start_simulation.hoc after running mknrndll. The files that model the individual figures have to be uncommented in start_simulation.hoc beforehand.
64.	CA1 pyramidal neuron: Persistent Na current mediates steep synaptic amplification (Hsu et al 2018)
		This paper shows that persistent sodium current critically contributes to the subthreshold nonlinear dynamics of CA1 pyramidal neurons and promotes rapidly reversible conversion between place-cell and silent-cell in the hippocampus. A simple model built with realistic axo-somatic voltage-gated sodium channels in CA1 (Carter et al., 2012; Neuron 75, 1081–1093) demonstrates that the biophysics of persistent sodium current is sufficient to explain the synaptic amplification effects. A full model built previously (Grienberger et al., 2017; Nature Neuroscience, 20(3): 417–426) with detailed morphology, ion channel types and biophysical properties of CA1 place cells naturally reproduces the steep voltage dependence of synaptic responses.
65.	Ca1 pyramidal neuron: reduction model (Marasco et al. 2012)
		"... Here we introduce a new, automatic and fast method to map realistic neurons into equivalent reduced models running up to >40 times faster while maintaining a very high accuracy of the membrane potential dynamics during synaptic inputs, and a direct link with experimental observables. The mapping of arbitrary sets of synaptic inputs, without additional fine tuning, would also allow the convenient and efficient implementation of a new generation of large-scale simulations of brain regions reproducing the biological variability observed in real neurons, with unprecedented advances to understand higher brain functions."
66.	CA1 pyramidal neuron: schizophrenic behavior (Migliore et al. 2011)
		NEURON files from the paper: A modeling study suggesting how a reduction in the context-dependent input on CA1 pyramidal neurons could generate schizophrenic behavior. by M. Migliore, I. De Blasi, D. Tegolo, R. Migliore, Neural Networks,(2011), doi:10.1016/j.neunet.2011.01.001. Starting from the experimentally supported assumption on hippocampal neurons we explore an experimentally testable prediction at the single neuron level. The model shows how and to what extent a pathological hypofunction of a contextdependent distal input on a CA1 neuron can generate hallucinations by altering the normal recall of objects on which the neuron has been previously tuned. The results suggest that a change in the context during the recall phase may cause an occasional but very significant change in the set of active dendrites used for features recognition, leading to a distorted perception of objects.
67.	CA1 pyramidal neuron: signal propagation in oblique dendrites (Migliore et al 2005)
		NEURON mod files from the paper: M. Migliore, M. Ferrante, GA Ascoli (2005). The model shows how the back- and forward propagation of action potentials in the oblique dendrites of CA1 neurons could be modulated by local properties such as morphology or active conductances.
68.	CA1 Pyramidal Neuron: Synaptic Scaling (London, Segev 2001)
		London and Segev (2001) discuss location dependent and location independent synaptic scaling in a model CA1 neuron with passive dendrites. The freely available text is followed by a critique by Maggee and Cook who comment that the London and Segev model is accurate and informative and however needs to be augmented by active channels in dendrites. Note: the zip files for this model are stored at the nature neuroscience website - Click above Supplementary Source Code in the readme.html in the model files
69.	CA1 pyramidal neuron: Synaptic Scaling (Magee, Cook 2000)
		Jeffrey Magee and Erik Cook found evidence in experiments and modeling that support the hypothesis that an increase in synaptic conductance for synapses at larger distances from the soma is responsible for reducing the location dependence (relative to the soma) of synapses.
70.	CA1 pyramidal neuron: synaptically-induced bAP predicts synapse location (Sterratt et al. 2012)
		This is an adaptation of Poirazi et al.'s (2003) CA1 model that is used to measure BAP-induced voltage and calcium signals in spines after simulated Schaffer collateral synapse stimulation. In the model, the peak calcium concentration is highly correlated with soma-synapse distance under a number of physiologically-realistic suprathreshold stimulation regimes and for a range of dendritic morphologies. There are also simulations demonstrating that peak calcium can be used to set up a synaptic democracy in a homeostatic manner, whereby synapses regulate their synaptic strength on the basis of the difference between peak calcium and a uniform target value.
71.	CA1 pyramidal neurons: binding properties and the magical number 7 (Migliore et al. 2008)
		NEURON files from the paper: Single neuron binding properties and the magical number 7, by M. Migliore, G. Novara, D. Tegolo, Hippocampus, in press (2008). In an extensive series of simulations with realistic morphologies and active properties, we demonstrate how n radial (oblique) dendrites of these neurons may be used to bind n inputs to generate an output signal. The results suggest a possible neural code as the most effective n-ple of dendrites that can be used for short-term memory recollection of persons, objects, or places. Our analysis predicts a straightforward physiological explanation for the observed puzzling limit of about 7 short-term memory items that can be stored by humans.
72.	CA1 pyramidal neurons: effect of external electric field from power lines (Cavarretta et al. 2014)
		The paper discusses the effects induced by an electric field at power lines frequency.
73.	CA1 pyramidal neurons: effects of Alzheimer (Culmone and Migliore 2012)
		The model predicts possible therapeutic treatments of Alzheimers's Disease in terms of pharmacological manipulations of channels' kinetic and activation properties. The results suggest how and which mechanism can be targeted by a drug to restore the original firing conditions. The simulations reproduce somatic membrane potential in control conditions, when 90% of membrane is affected by AD (Fig.4A of the paper), and after treatment (Fig.4B of the paper).
74.	CA1 pyramidal neurons: effects of Kv7 (M-) channels on synaptic integration (Shah et al. 2011)
		NEURON mod files from the paper: Shah et al., 2011. In this study, using a combination of electrophysiology and computational modelling, we show that these channels selectively influence peri-somatic but not dendritic post-synaptic excitatory synaptic potential (EPSP) integration in CA1 pyramidal cells. This may be important for their relative contributions to physiological processes such as synaptic plasticity as well as patho-physiological conditions such as epilepsy.
75.	Ca2+ requirements for Long-Term Depression in Purkinje Cells (Criseida Zamora et al 2018)
		An updated stochastic model of cerebellar Long-Term Depression (LTD) to study the requirements of calcium to induce LTD. Calcium signal is generated as a train of calcium pulses and this can be modulated by its amplitude, frequency, width and number of pulses. CaMKII activation and its regulatory pathway are added to an earlier published model to study the sensitivity to calcium frequency. The model is useful to investigate systematically the dependence of LTD induction on calcium stimuli parameters.
76.	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. ..."
77.	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.
78.	Ca3 pyramidal neuron: membrane response near rest (Hemond et al. 2009)
		In this paper, the model was used to show how the temporal summation of excitatory inputs in CA3 pyramidal neurons was affected by the presence of Ih in the dendrites in a frequency- and distance-dependent fashion.
79.	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. …"
80.	Calcium response prediction in the striatal spines depending on input timing (Nakano et al. 2013)
		We construct an electric compartment model of the striatal medium spiny neuron with a realistic morphology and predict the calcium responses in the synaptic spines with variable timings of the glutamatergic and dopaminergic inputs and the postsynaptic action potentials. The model was validated by reproducing the responses to current inputs and could predict the electric and calcium responses to glutamatergic inputs and back-propagating action potential in the proximal and distal synaptic spines during up and down states.
81.	Calcium waves and mGluR-dependent synaptic plasticity in CA1 pyr. neurons (Ashhad & Narayanan 2013)
		A morphologically realistic, conductance-based model equipped with kinetic schemes that govern several calcium signalling modules and pathways in CA1 pyramidal neurons
82.	Cell-type specific integration of feedforward and feedback synaptic inputs (Ridner et al, 2022)
		Simple compartmental model is used to explore and predict channel mechanisms that underlie differences in non-integration of synaptic inputs to posterior parietal cortex pyramidal subtypes, namely regular spiking cell and intrinsically bursting cell.
83.	Central Nervous System tadpole model in Matlab and NEURON-Python (Ferrario et al, 2021)
		This is the source code for three compuational models used for generating connectivity and swimming dynamics of spinal cord and hindbrain neurons in the Xenopus tadpoles using biological data. The model reproduces the initiation, continuation, termination and accelaration of forward swimming.
84.	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. ..."
85.	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.
86.	Cerebellar granule cell (Masoli et al 2020)
		"The cerebellar granule cells (GrCs) are classically described as a homogeneous neuronal population discharging regularly without adaptation. We show that GrCs in fact generate diverse response patterns to current injection and synaptic activation, ranging from adaptation to acceleration of firing. Adaptation was predicted by parameter optimization in detailed computational models based on available knowledge on GrC ionic channels. The models also predicted that acceleration required additional mechanisms. We found that yet unrecognized TRPM4 currents specifically accounted for firing acceleration and that adapting GrCs outperformed accelerating GrCs in transmitting high-frequency mossy fiber (MF) bursts over a background discharge. This implied that GrC subtypes identified by their electroresponsiveness corresponded to specific neurotransmitter release probability values. Simulations showed that fine-tuning of pre- and post-synaptic parameters generated effective MF-GrC transmission channels, which could enrich the processing of input spike patterns and enhance spatio-temporal recoding at the cerebellar input stage."
87.	Cerebellar long-term depression (LTD) (Antunes and De Schutter 2012)
		Many cellular processes involve small number of molecules and undergo stochastic fluctuations in their levels of activity. Among these processes is cerebellar long-term depression (LTD), a form of synaptic plasticity expressed as a reduction in the number of synaptic AMPA receptors (AMPARs) in Purkinje cells. Using a stochastic model of the signaling network and mechanisms of AMPAR trafficking involved in LTD, we show that the network activity in single synapses switches between two discrete stable states (LTD and non-LTD). Stochastic fluctuations affecting more intensely the level of activity of a few components of the network lead to the probabilistic induction of LTD and threshold dithering. The non-uniformly distributed stochasticity of the network allows the stable occurrence of several different macroscopic levels of depression, determining the experimentally observed sigmoidal relationship between the magnitude of depression and the concentration of the triggering signal.
88.	Cerebellar Model for the Optokinetic Response (Kim and Lim 2021)
		We consider a cerebellar spiking neural network for the optokinetic response (OKR). Individual granule (GR) cells exhibit diverse spiking patterns which are in-phase, anti-phase, or complex out-of-phase with respect to their population-averaged firing activity. Then, these diversely-recoded signals via parallel fibers (PFs) from GR cells are effectively depressed by the error-teaching signals via climbing fibers from the inferior olive which are also in-phase ones. Synaptic weights at in-phase PF-Purkinje cell (PC) synapses of active GR cells are strongly depressed via strong long-term depression (LTD), while those at anti-phase and complex out-of-phase PF-PC synapses are weakly depressed through weak LTD. This kind of ‘‘effective’’ depression at the PF-PC synapses causes a big modulation in firings of PCs, which then exert effective inhibitory coordination on the vestibular nucleus (VN) neuron (which evokes OKR). For the firing of the VN neuron, the learning gain degree, corresponding to the modulation gain ratio, increases with increasing the learning cycle, and it saturates.
89.	Cerebellar Nucleus Neuron (Steuber, Schultheiss, Silver, De Schutter & Jaeger, 2010)
		This is the GENESIS 2.3 implementation of a multi-compartmental deep cerebellar nucleus (DCN) neuron model with a full dendritic morphology and appropriate active conductances. We generated a good match of our simulations with DCN current clamp data we recorded in acute slices, including the heterogeneity in the rebound responses. We then examined how inhibitory and excitatory synaptic input interacted with these intrinsic conductances to control DCN firing. We found that the output spiking of the model reflected the ongoing balance of excitatory and inhibitory input rates and that changing the level of inhibition performed an additive operation. Rebound firing following strong Purkinje cell input bursts was also possible, but only if the chloride reversal potential was more negative than -70 mV to allow de-inactivation of rebound currents. Fast rebound bursts due to T-type calcium current and slow rebounds due to persistent sodium current could be differentially regulated by synaptic input, and the pattern of these rebounds was further influenced by HCN current. Our findings suggest that active properties of DCN neurons could play a crucial role for signal processing in the cerebellum.
90.	Cerebellum granule cell FHF (Dover et al. 2016)
		"Neurons in vertebrate central nervous systems initiate and conduct sodium action potentials in distinct subcellular compartments that differ architecturally and electrically. Here, we report several unanticipated passive and active properties of the cerebellar granule cell's unmyelinated axon. Whereas spike initiation at the axon initial segment relies on sodium channel (Nav)-associated fibroblast growth factor homologous factor (FHF) proteins to delay Nav inactivation, distal axonal Navs show little FHF association or FHF requirement for high-frequency transmission, velocity and waveforms of conducting action potentials. ...'
91.	Circadian rhythmicity shapes astrocyte morphology and neuronal function in CA1 (McCauley et al 2020)
		Most animal species operate according to a 24-hour period set by the suprachiasmatic nucleus (SCN) of the hypothalamus. The rhythmic activity of the SCN modulates hippocampal-dependent memory, but the molecular and cellular mechanisms that account for this effect remain largely unknown. In McCauley et al. 2020 [1], we identify cell-type specific structural and functional changes that occur with circadian rhythmicity in neurons and astrocytes in hippocampal area CA1. Pyramidal neurons change the surface expression of NMDA receptors. Astrocytes change their proximity clustered excitatory synaptic inputs, ultimately shaping hippocampal-dependent learning in vivo. We identify to synapses. Together, these phenomena alter glutamate clearance, receptor activation and integration of temporally corticosterone as a key contributor to changes in synaptic strength. These findings highlight important mechanisms through which neurons and astrocytes modify the molecular composition and structure of the synaptic environment, contribute to the local storage of information in the hippocampus and alter the temporal dynamics of cognitive processing. [1] "Circadian modulation of neurons and astrocytes controls synaptic plasticity in hippocampal area CA1" by J.P. McCauley, M.A. Petroccione, L.Y. D’Brant, G.C. Todd, N. Affinnih, J.J. Wisnoski, S. Zahid, S. Shree, A.A. Sousa, R.M. De Guzman, R. Migliore, A. Brazhe, R.D. Leapman, A. Khmaladze, A. Semyanov, D.G. Zuloaga, M. Migliore and A. Scimemi. Cell Reports (2020), https://doi.org/10.1016/j.celrep.2020.108255
92.	Coding of stimulus frequency by latency in thalamic networks (Golomb et al 2005)
		The paper presents models of the rat vibrissa processing system including the posterior medial (POm) thalamus, ventroposterior medial (VPm) thalamus, and GABAB- mediated feedback inhibition from the reticular thalamic (Rt) nucleus. A clear match between the experimentally measured spike-rates and the numerically calculated rates for the full model occurs when VPm thalamus receives stronger brainstem input and weaker GABAB-mediated inhibition than POm thalamus.
93.	Coincident glutamatergic depolarization effects on Cl- dynamics (Lombardi et al, 2021)
		"... we used compartmental biophysical models of Cl- dynamics simulating either a simple ball-and-stick topology or a reconstructed CA3 neuron. These computational experiments demonstrated that glutamatergic co-stimulation enhances GABA receptor-mediated Cl- influx at low and attenuates or reverses the Cl- efflux at high initial [Cl-]i. The size of glutamatergic influence on GABAergic Cl--fluxes depends on the conductance, decay kinetics, and localization of glutamatergic inputs. Surprisingly, the glutamatergic shift in GABAergic Cl--fluxes is invariant to latencies between GABAergic and glutamatergic inputs over a substantial interval..."
94.	Coincident signals in Olfactory Bulb Granule Cell spines (Aghvami et al 2019)
		"In the mammalian olfactory bulb, the inhibitory axonless granule cells (GCs) feature reciprocal synapses that interconnect them with the principal neurons of the bulb, mitral, and tufted cells. These synapses are located within large excitable spines that can generate local action potentials (APs) upon synaptic input (“spine spike”). Moreover, GCs can fire global APs that propagate throughout the dendrite. Strikingly, local postsynaptic Ca2+ entry summates mostly linearly with Ca2+ entry due to coincident global APs generated by glomerular stimulation, although some underlying conductances should be inactivated. We investigated this phenomenon by constructing a compartmental GC model to simulate the pairing of local and global signals as a function of their temporal separation ?t. These simulations yield strongly sublinear summation of spine Ca2+ entry for the case of perfect coincidence ?t = 0 ms. ..."
95.	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."
96.	Comparison of full and reduced globus pallidus models (Hendrickson 2010)
		In this paper, we studied what features of realistic full model activity patterns can and cannot be preserved by morphologically reduced models. To this end, we reduced the morphological complexity of a full globus pallidus neuron model possessing active dendrites and compared its spontaneous and driven responses to those of the reduced models.
97.	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.
98.	Comprehensive models of human cortical pyramidal neurons (Eyal et al 2018)
		"We present detailed models of pyramidal cells from human neocortex, including models on their excitatory synapses, dendritic spines, dendritic NMDA- and somatic/axonal Na+ spikes that provided new insights into signal processing and computational capabilities of these principal cells. Six human layer 2 and layer 3 pyramidal cells (HL2/L3 PCs) were modeled, integrating detailed anatomical and physiological data from both fresh and postmortem tissues from human temporal cortex. The models predicted particularly large AMPA- and NMDA- conductances per synaptic contact (0.88 nS and 1.31nS, respectively) and a steep dependence of the NMDA-conductance on voltage..."
99.	Conductance based model for short term plasticity at CA3-CA1 synapses (Mukunda & Narayanan 2017)
		We develop a new biophysically rooted, physiologically constrained conductance-based synaptic model to mechanistically account for short-term facilitation and depression, respectively through residual calcium and transmitter depletion kinetics. The model exhibits different synaptic filtering profiles upon changing certain parameters in the base model. We show degenercy in achieving similar plasticity profiles with different presynaptic parameters. Finally, by virtually knocking out certain conductances, we show the differential contribution of conductances.
100.	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. ..."
101.	Cortical Basal Ganglia Network Model during Closed-loop DBS (Fleming et al 2020)
		We developed a computational model of the cortical basal ganglia network to investigate closed-loop control of deep brain stimulation (DBS) for Parkinson’s disease (PD). The cortical basal ganglia network model incorporates the (i) the extracellular DBS electric field, (ii) antidromic and orthodromic activation of STN afferent fibers, (iii) the LFP detected at non-stimulating contacts on the DBS electrode and (iv) temporal variation of network beta-band activity within the thalamo-cortico-basal ganglia loop. The model facilitates investigation of clinically-viable closed-loop DBS control approaches, modulating either DBS amplitude or frequency, using an LFP derived measure of network beta-activity.
102.	Cortical model with reinforcement learning drives realistic virtual arm (Dura-Bernal et al 2015)
		We developed a 3-layer sensorimotor cortical network of consisting of 704 spiking model-neurons, including excitatory, fast-spiking and low-threshold spiking interneurons. Neurons were interconnected with AMPA/NMDA, and GABAA synapses. We trained our model using spike-timing-dependent reinforcement learning to control a virtual musculoskeletal human arm, with realistic anatomical and biomechanical properties, to reach a target. Virtual arm position was used to simultaneously control a robot arm via a network interface.
103.	Cortical network model of posttraumatic epileptogenesis (Bush et al 1999)
		This simulation from Bush, Prince, and Miller 1999 shows the epileptiform response (Fig. 6C) to a brief single stimulation in a 500 cell network of multicompartment models, some of which have active dendrites. The results which I obtained under Redhat Linux is shown in result.gif. Original 1997 code from Paul Bush modified slightly by Bill Lytton to make it work with current version of NEURON (5.7.139). Thanks to Paul Bush and Ken Miller for making the code available.
104.	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
105.	Deconstruction of cortical evoked potentials generated by subthalamic DBS (Kumaravelu et al 2018)
		"... High frequency deep brain stimulation (DBS) of the subthalamic nucleus (STN) suppresses parkinsonian motor symptoms and modulates cortical activity. ... Cortical evoked potentials (cEP) generated by STN DBS reflect the response of cortex to subcortical stimulation, and the goal was to determine the neural origin of cEP using a two-step approach. First, we recorded cEP over ipsilateral primary motor cortex during different frequencies of STN DBS in awake healthy and unilateral 6-OHDA lesioned parkinsonian rats. Second, we used a biophysically-based model of the thalamocortical network to deconstruct the neural origin of the cEP. The in vivo cEP included short (R1), intermediate (R2) and long-latency (R3) responses. Model-based cortical responses to simulated STN DBS matched remarkably well the in vivo responses. R1 was generated by antidromic activation of layer 5 pyramidal neurons, while recurrent activation of layer 5 pyramidal neurons via excitatory axon collaterals reproduced R2. R3 was generated by polysynaptic activation of layer 2/3 pyramidal neurons via the cortico-thalamic-cortical pathway. Antidromic activation of the hyperdirect pathway and subsequent intracortical and cortico-thalamo-cortical synaptic interactions were sufficient to generate cEP by STN DBS, and orthodromic activation through basal ganglia-thalamus-cortex pathways was not required. These results demonstrate the utility of cEP to determine the neural elements activated by STN DBS that might modulate cortical activity and contribute to the suppression of parkinsonian symptoms."
106.	Dendritic action potentials and computation in human layer 2/3 cortical neurons (Gidon et al 2020)
		This code reproduces figs 3 and S9 in Dendritic action potentials in layer 2/3 pyramidal neurons of the human neocortex.
107.	Dentate Gyrus Feed-forward inhibition (Ferrante et al. 2009)
		In this paper, the model was used to show how that FFI can change a steeply sigmoidal input-output (I/O) curve into a double-sigmoid typical of buffer systems.
108.	Dentate gyrus granule cell: subthreshold signal processing (Schmidt-Hieber et al. 2007)
		Detailed compartmental cable models of 8 hippocampal granule cells of adult mice were obtained from dual patch-clamp whole-cell recordings and subsequent 3D reconstructions. This code allows to reproduce figures 6-8 from the paper.
109.	Dentate Gyrus model including Granule cells with dendritic compartments (Chavlis et al 2017)
		Here we investigate the role of dentate granule cell dendrites in pattern separation. The model consists of point neurons (Integrate and fire) and in principal neurons, the granule cells, we have incorporated various number of dendrites.
110.	Dentate gyrus network model (Santhakumar et al 2005)
		Mossy cell loss and mossy fiber sprouting are two characteristic consequences of repeated seizures and head trauma. However, their precise contributions to the hyperexcitable state are not well understood. Because it is difficult, and frequently impossible, to independently examine using experimental techniques whether it is the loss of mossy cells or the sprouting of mossy fibers that leads to dentate hyperexcitability, we built a biophysically realistic and anatomically representative computational model of the dentate gyrus to examine this question. The 527-cell model, containing granule, mossy, basket, and hilar cells with axonal projections to the perforant-path termination zone, showed that even weak mossy fiber sprouting (10-15% of the strong sprouting observed in the pilocarpine model of epilepsy) resulted in the spread of seizure-like activity to the adjacent model hippocampal laminae after focal stimulation of the perforant path. See reference for more and details.
111.	Dentate gyrus network model pattern separation and granule cell scaling in epilepsy (Yim et al 2015)
		The dentate gyrus (DG) is thought to enable efficient hippocampal memory acquisition via pattern separation. With patterns defined as spatiotemporally distributed action potential sequences, the principal DG output neurons (granule cells, GCs), presumably sparsen and separate similar input patterns from the perforant path (PP). In electrophysiological experiments, we have demonstrated that during temporal lobe epilepsy (TLE), GCs downscale their excitability by transcriptional upregulation of ‘leak’ channels. Here we studied whether this cell type-specific intrinsic plasticity is in a position to homeostatically adjust DG network function. We modified an established conductance-based computer model of the DG network such that it realizes a spatiotemporal pattern separation task, and quantified its performance with and without the experimentally constrained leaky GC phenotype. ...
112.	DG adult-born granule cell: nonlinear a5-GABAARs control AP firing (Lodge et al, 2021)
		GABA can depolarize immature neurons close to the action potential (AP) threshold in development and adult neurogenesis. Nevertheless, GABAergic synapses effectively inhibit AP firing in newborn granule cells of the adult hippocampus as early as 2 weeks post mitosis. Parvalbumin and dendrite-targeting somatostatin interneurons activate a5-subunit containing GABAA receptors (a5-GABAARs) in young neurons, which show a voltage dependent conductance profile with increasing conductance around the AP threshold. The present computational models show that the depolarized GABA reversal potential promotes NMDA receptor activation. However, the voltage-dependent conductance of a5-GABAARs in young neurons is crucial for inhibition of AP firing to generate balanced and sparse firing activity.
113.	Differential modulation of pattern and rate in a dopamine neuron model (Canavier and Landry 2006)
		"A stylized, symmetric, compartmental model of a dopamine neuron in vivo shows how rate and pattern can be modulated either concurrently or differentially. If two or more parameters in the model are varied concurrently, the baseline firing rate and the extent of bursting become decorrelated, which provides an explanation for the lack of a tight correlation in vivo and is consistent with some independence of the mechanisms that generate baseline firing rates versus bursting. ..." See paper for more and details.
114.	Discrimination on behavioral time-scales mediated by reaction-diffusion in dendrites (Bhalla 2017)
		Sequences of events are ubiquitous in sensory, motor, and cognitive function. Key computational operations, including pattern recognition, event prediction, and plasticity, involve neural discrimination of spatio-temporal sequences. Here we show that synaptically-driven reaction diffusion pathways on dendrites can perform sequence discrimination on behaviorally relevant time-scales. We used abstract signaling models to show that selectivity arises when inputs at successive locations are aligned with, and amplified by, propagating chemical waves triggered by previous inputs. We incorporated biological detail using sequential synaptic input onto spines in morphologically, electrically, and chemically detailed pyramidal neuronal models based on rat data.
115.	Distal inhibitory control of sensory-evoked excitation (Egger, Schmitt et al. 2015)
		Model of a cortical layer (L) 2 pyramidal neuron embedded in an anatomically realistic network of two barrel columns in rat vibrissal cortex. This model is used to investigate the effects of spatially and temporally specific inhibition from L1 inhibitory interneurons on the sensory-evoked subthreshold responses of the L2 pyramidal neuron, and can be used to create simulation results underlying Figures 3D, 4B, 4C and 4E from (Egger, Schmitt et al. 2015).
116.	Distance-dependent synaptic strength in CA1 pyramidal neurons (Menon et al. 2013)
		Menon et al. (2013) describes the experimentally-observed variation in synaptic AMPA and NMDA conductance as a function of distance from the soma. This model explores the effect of this variation on somatic EPSPs and dendritic spike initiation, as compared to the case of uniform AMPA and NMDA conductance.
117.	Distinct integration properties of noisy inputs in active dendritic subunits (Poleg-Polsky 2019)
		The brain operates surprisingly well despite the noisy nature of individual neurons. The central mechanism for noise mitigation in the nervous system is thought to involve averaging over multiple noise-corrupted inputs. Subsequently, there has been considerable interest recently to identify noise structures that can be integrated linearly in a way that preserves reliable signal encoding. By analyzing realistic synaptic integration in biophysically accurate neuronal models, I report a complementary de-noising approach that is mediated by focal dendritic spikes. Dendritic spikes might seem to be unlikely candidates for noise reduction due to their miniscule integration compartments and poor averaging abilities. Nonetheless, the extra thresholding step introduced by dendritic spike generation increases neuronal tolerance for a broad category of noise structures, some of which cannot be resolved well with averaging. This property of active dendrites compensates for compartment size constraints and expands the repertoire of conditions that can be processed by neuronal populations.
118.	Dopamine-modulated medium spiny neuron, reduced model (Humphries et al. 2009)
		We extended Izhikevich's reduced model of the striatal medium spiny neuron (MSN) to account for dopaminergic modulation of its intrinsic ion channels and synaptic inputs. We tuned our D1 and D2 receptor MSN models using data from a recent (Moyer et al, 2007) large-scale compartmental model. Our new models capture the input-output relationships for both current injection and spiking input with remarkable accuracy, despite the order of magnitude decrease in system size. They also capture the paired pulse facilitation shown by MSNs. Our dopamine models predict that synaptic effects dominate intrinsic effects for all levels of D1 and D2 receptor activation. Our analytical work on these models predicts that the MSN is never bistable. Nonetheless, these MSN models can produce a spontaneously bimodal membrane potential similar to that recently observed in vitro following application of NMDA agonists. We demonstrate that this bimodality is created by modelling the agonist effects as slow, irregular and massive jumps in NMDA conductance and, rather than a form of bistability, is due to the voltage-dependent blockade of NMDA receptors
119.	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.
120.	Duration-tuned neurons from the inferior colliculus of the big brown bat (Aubie et al. 2009)
		dtnet is a generalized neural network simulator written in C++ with an easy to use XML description language to generate arbitrary neural networks and then run simulations covering many different parameter values. For example, you can specify ranges of parameter values for several different connection weights and then automatically run simulations over all possible parameters. Graphing ability is built in as long as the free, open-source, graphing application GLE (http://glx.sourceforge.net/) is installed. Included in the examples folder are simulation descriptions that were used to generate the results in Aubie et al. (2009). Refer to the README file for instructions on compiling and running these examples. The most recent source code can be obtained from GitHub: <a href="https://github.com/baubie/dtnet">https://github.com/baubie/dtnet</a>
121.	Duration-tuned neurons from the inferior colliculus of vertebrates (Aubie et al. 2012)
		These models reproduce the responses of duration-tuned neurons in the auditory midbrain of the big brown bat, the rat, the mouse and the frog (Aubie et al. 2012). They are written in the Python interface to NEURON and a subset of the figures from Aubie et al. (2012) are pre-set in run.py (raw data is generated and a separate graphing program must be used to visualize the results).
122.	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."
123.	Effect of the initial synaptic state on the probability to induce LTP and LTD (Migliore et al. 2015)
		NEURON mod files from the paper: M. Migliore, et al. (2015). In this paper, we investigate the possibility that the experimental protocols on synaptic plasticity may result in different consequences (e.g., LTD instead of LTP), according to the initial conditions of the stimulated synapses, and can generate confusing results. Using biophysical models of synaptic plasticity and hippocampal CA1 pyramidal neurons, we study how, why, and to what extent EPSPs observed at the soma after induction of LTP/LTD reflects the actual (local) synaptic state. The model and the results suggest a physiologically plausible explanation of why LTD induction is experimentally difficult, and they offer experimentally testable predictions on the stimulation protocols that may be more effective.
124.	Effects of Dopamine Modulation and KIR Inactivation in NAc Medium Spiny Neurons (Steephen 2011)
		Due to the involvement of nucleus accumbens (NAc) medium spiny neurons (MSNs) in diverse behaviors, their excitability changes can have broad functional significance. Dopamine modulates the biophysical behavior of MSNs. In ~40% of MSNs, inward rectifying potassium (KIR) currents inactivate significantly, imparting greater excitability. Employing a 189-compartment computational model of the MSN and using spatiotemporally distributed synaptic inputs, the regulation of excitability by KIR inactivation and dopaminergic modulation was investigated and quantitatively characterized. It was shown that by forming different combinations, these regulating agents could fine tune MSN excitability across a wide range. With existing evidence indicating MSNs with and without KIR inactivation to be the likely targets for D2- and D1-receptor mediated modulations, respectively, the present findings suggest that dopaminergic channel modulation may intensify the existing excitability difference between them by suppressing the excitability of MSNs without KIR inactivation while further enhancing the excitability of the more excitable MSNs with KIR inactivation. On the other hand, the combined modulation of channels and synapses by dopamine may reverse the relative excitability of one cell type with respect to the other. This model contains a complete biophysical model of MSN cell. The application allows the user to vary the cell properties by choosing the type of KIR channels included (inKIR or non-inKIR), the type of Dopamine receptors (D1R or D2R) and the modulation mechanism (Intrinsic modulation , Intrinsic-synaptic modulation, or No modulation). The user can also choose between the single pulse current clamp stimulation or a physiologically realistic synaptic stimulation scheme. More details are available in the Help provided with the application.
125.	Effects of electric fields on cognitive functions (Migliore et al 2016)
		The paper discusses the effects induced by an electric field at power lines frequency on neuronal activity during cognitive processes.
126.	Effects of increasing CREB on storage and recall processes in a CA1 network (Bianchi et al. 2014)
		Several recent results suggest that boosting the CREB pathway improves hippocampal-dependent memory in healthy rodents and restores this type of memory in an AD mouse model. However, not much is known about how CREB-dependent neuronal alterations in synaptic strength, excitability and LTP can boost memory formation in the complex architecture of a neuronal network. Using a model of a CA1 microcircuit, we investigate whether hippocampal CA1 pyramidal neuron properties altered by increasing CREB activity may contribute to improve memory storage and recall. With a set of patterns presented to a network, we find that the pattern recall quality under AD-like conditions is significantly better when boosting CREB function with respect to control. The results are robust and consistent upon increasing the synaptic damage expected by AD progression, supporting the idea that the use of CREB-based therapies could provide a new approach to treat AD.
127.	Effects of KIR current inactivation in NAc Medium Spiny Neurons (Steephen and Manchanda 2009)
		"Inward rectifying potassium (KIR) currents in medium spiny (MS) neurons of nucleus accumbens inactivate significantly in ~40% of the neurons but not in the rest, which may lead to differences in input processing by these two groups. Using a 189-compartment computational model of the MS neuron, we investigate the influence of this property using injected current as well as spatiotemporally distributed synaptic inputs. Our study demonstrates that KIR current inactivation facilitates depolarization, firing frequency and firing onset in these neurons. ..."
128.	Effects of neural morphology on global and focal NMDA-spikes (Poleg-Polsky 2015)
		This entry contains the NEURON files required to recreate figures 4-8 of the paper "Effects of Neural Morphology and Input Distribution on Synaptic Processing by Global and Focal NMDA-spikes" by Alon Poleg-Polsky
129.	Effects of spinal cord stimulation on WDR dorsal horn network (Zhang et al 2014)
		" ... To study the mechanisms underlying SCS (Spinal cord stimulation), we constructed a biophysically-based network model of the dorsal horn circuit consisting of interconnected dorsal horn interneurons and a wide dynamic range (WDR) projection neuron and representations of both local and surround receptive field inhibition. We validated the network model by reproducing cellular and network responses relevant to pain processing including wind-up, A-fiber mediated inhibition, and surround receptive field inhibition. ..." See paper for more.
130.	Efficient simulation environment for modeling large-scale cortical processing (Richert et al. 2011)
		"We have developed a spiking neural network simulator, which is both easy to use and computationally efficient, for the generation of large-scale computational neuroscience models. The simulator implements current or conductance based Izhikevich neuron networks, having spike-timing dependent plasticity and short-term plasticity. ..."
131.	Electrostimulation to reduce synaptic scaling driven progression of Alzheimers (Rowan et al. 2014)
		"... As cells die and synapses lose their drive, remaining cells suffer an initial decrease in activity. Neuronal homeostatic synaptic scaling then provides a feedback mechanism to restore activity. ... The scaling mechanism increases the firing rates of remaining cells in the network to compensate for decreases in network activity. However, this effect can itself become a pathology, ... Here, we present a mechanistic explanation of how directed brain stimulation might be expected to slow AD progression based on computational simulations in a 470-neuron biomimetic model of a neocortical column. ... "
132.	Electrotonic transform and EPSCs for WT and Q175+/- spiny projection neurons (Goodliffe et al 2018)
		This model achieves electrotonic transform and computes mean inward and outward attenuation from 0 to 500 Hz input; and randomly activates synapses along dendrites to simulate AMPAR mediated EPSCs. For electrotonic analysis, in Elec folder, the entry file is MSNelec_transform.hoc. For EPSC simulation, in Syn folder, the entry file is randomepsc.hoc. Run read_EPSCsims_mdb_alone.m next with the simulated parameter values specified to compute the mean EPSC.
133.	ELL pyramidal neuron (Simmonds and Chacron 2014)
		network model of ELL pyramidal neurons receiving both feedforward and feedback inputs
134.	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. ..."
135.	Emergent properties of networks of biological signaling pathways (Bhalla, Iyengar 1999)
		Biochemical signaling networks were constructed with experimentally obtained constants and analyzed by computational methods to understand their role in complex biological processes. These networks exhibit emergent properties such as integration of signals across multiple time scales, generation of distinct outputs depending on input strength and duration, and self-sustaining feedback loops. Properties of signaling networks raise the possibility that information for "learned behavior" of biological systems may be stored within intracellular biochemical reactions that comprise signaling pathways.
136.	Encoding and retrieval in a model of the hippocampal CA1 microcircuit (Cutsuridis et al. 2009)
		This NEURON code implements a small network model (100 pyramidal cells and 4 types of inhibitory interneuron) of storage and recall of patterns in the CA1 region of the mammalian hippocampus. Patterns of PC activity are stored either by a predefined weight matrix generated by Hebbian learning, or by STDP at CA3 Schaffer collateral AMPA synapses.
137.	Endocannabinoid dynamics gate spike-timing dependent depression and potentiation (Cui et al 2016)
		The endocannabinoid (eCB) system is considered involved in synaptic depression. Recent reports have also linked eCBs to synaptic potentiation. However it is not known how eCB signaling may support such bidirectionality. To question the mechanisms of this phenomena in spike-timing dependent plasticity (STDP) at corticostriatal synapses, we combined electrophysiology experiments with biophysical modeling. We demonstrate that STDP is controlled by eCB levels and dynamics: prolonged and moderate levels of eCB lead to eCB-mediated long-term depression (eCB-tLTD) while short and large eCB transients produce eCB-mediated long-term potentiation (eCB-tLTP). Therefore, just like neurotransmitters glutamate or GABA, eCB form a bidirectional system.
138.	Epilepsy may be caused by very small functional changes in ion channels (Thomas et al. 2009)
		We used a previously published model of the dentate gyrus with varying degrees of mossy fibre sprouting.We preformed a sensitivity analysis where we systematically varied individual properties of ion channels. The results predict that genetic variations in the properties of sodium channels are likely to have the biggest impact on network excitability. Furthermore, these changes may be as small as 1mV, which is currently undetectable using standard experimental practices.
139.	Estimation and Production of Time Intervals (Migliore et al 2001)
		NEURON model files from the paper M. Migliore, L. Messineo, M. Cardaci, G.F. Ayala, Quantitative modeling of perception and production of time intervals, J.Neurophysiol. 86, 2754-2760 (2001). Contact michele.migliore@pa.ibf.cnr.it if you have any questions about the implementation of the model.
140.	Excitability of DA neurons and their regulation by synaptic input (Morozova et al. 2016a, 2016b)
		This code contains conductance-based models of Dopaminergic (DA) and GABAergic neurons, used in Morozova et al 2016 PLOS Computational Biology paper in order to study the type of excitability of the DA neurons and how it is influenced by the intrinsic and synaptic currents. We identified the type of excitability by calculating bifurcation diagrams and F-I curves using XPP file. This model was also used in Morozova et al 2016 J. Neurophysiology paper in order to study the effect of synchronization in GABAergic inputs on the firing dynamics of the DA neuron.
141.	Excitatory synaptic interactions in pyramidal neuron dendrites (Behabadi et al. 2012)
		" ... We hypothesized that if two excitatory pathways bias their synaptic projections towards proximal vs. distal ends of the basal branches, the very different local spike thresholds and attenuation factors for inputs near and far from the soma might provide the basis for a classical-contextual functional asymmetry. Supporting this possibility, we found both in compartmental models and electrophysiological recordings in brain slices that the responses of basal dendrites to spatially separated inputs are indeed strongly asymmetric. ..."
142.	Excitotoxic loss of dopaminergic cells in PD (Muddapu et al 2019)
		"... A couple of the proposed mechanisms, however, show potential for the development of a novel line of PD (Parkinson's disease) therapeutics. One of these mechanisms is the peculiar metabolic vulnerability of SNc (Substantia Nigra pars compacta) cells compared to other dopaminergic clusters; the other is the SubThalamic Nucleus (STN)-induced excitotoxicity in SNc. To investigate the latter hypothesis computationally, we developed a spiking neuron network-model of SNc-STN-GPe system. In the model, prolonged stimulation of SNc cells by an overactive STN leads to an increase in ‘stress’ variable; when the stress in a SNc neuron exceeds a stress threshold, the neuron dies. The model shows that the interaction between SNc and STN involves a positive-feedback due to which, an initial loss of SNc cells that crosses a threshold causes a runaway-effect, leading to an inexorable loss of SNc cells, strongly resembling the process of neurodegeneration. The model further suggests a link between the two aforementioned mechanisms of SNc cell loss. Our simulation results show that the excitotoxic cause of SNc cell loss might initiate by weak-excitotoxicity mediated by energy deficit, followed by strong-excitotoxicity, mediated by a disinhibited STN. A variety of conventional therapies were simulated to test their efficacy in slowing down SNc cell loss. Among them, glutamate inhibition, dopamine restoration, subthalamotomy and deep brain stimulation showed superior neuroprotective-effects in the proposed model."
143.	Fast AMPA receptor signaling (Geiger et al 1997)
		Glutamatergic transmission at a principal neuron-interneuron synapse was investigated by dual whole-cell patch-clamp recording in rat hippocampal slices combined with morphological analysis and modeling. Simulations based on a compartmental model of the interneuron indicated that the rapid postsynaptic conductance change determines the shape and the somatodendritic integration of EPSPs, thus enabling interneurons to detect synchronous principal neuron activity.
144.	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.
145.	Feedforward heteroassociative network with HH dynamics (Lytton 1998)
		Using the original McCulloch-Pitts notion of simple on and off spike coding in lieu of rate coding, an Anderson-Kohonen artificial neural network (ANN) associative memory model was ported to a neuronal network with Hodgkin-Huxley dynamics.
146.	Fluctuating synaptic conductances recreate in-vivo-like activity (Destexhe et al 2001)
		This model (and experiments) reported in Destexhe, Rudolh, Fellous, and Sejnowski (2001) support the hypothesis that many of the characteristics of cortical neurons in vivo can be explained by fast glutamatergic and GABAergic conductances varying stochastically. Some of these cortical neuron characteristics of fluctuating synaptic origin are a depolarized membrane potential, the presence of high-amplitude membrane potential fluctuations, a low input resistance and irregular spontaneous firing activity. In addition, the point-conductance model could simulate the enhancement of responsiveness due to background activity. For more information please contact Alain Destexhe. email: Destexhe@iaf.cnrs-gif.fr
147.	FS Striatal interneuron: K currents solve signal-to-noise problems (Kotaleski et al 2006)
		... We show that a transient potassium (KA) current allows the Fast Spiking (FS) interneuron to strike a balance between sensitivity to correlated input and robustness to noise, thereby increasing its signal-to-noise ratio (SNR). First, a compartmental FS neuron model was created to match experimental data from striatal FS interneurons in cortex–striatum–substantia nigra organotypic cultures. Densities of sodium, delayed rectifier, and KA channels were optimized to replicate responses to somatic current injection. Spontaneous AMPA and GABA synaptic currents were adjusted to the experimentally measured amplitude, rise time, and interevent interval histograms. Second, two additional adjustments were required to emulate the remaining experimental observations. GABA channels were localized closer to the soma than AMPA channels to match the synaptic population reversal potential. Correlation among inputs was required to produce the observed firing rate during up-states. In this final model, KA channels were essential for suppressing down-state spikes while allowing reliable spike generation during up-states. ... Our results suggest that KA channels allow FS interneurons to operate without a decrease in SNR during conditions of increased dopamine, as occurs in response to reward or anticipated reward. See paper for more and details.
148.	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. ..."
149.	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). ..."
150.	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.
151.	Gamma-beta alternation in the olfactory bulb (David, Fourcaud-Trocmé et al., 2015)
		This model, a simplified olfactory bulb network with mitral and granule cells, proposes a framework for two regimes of oscillation in the olfactory bulb: 1 - a weak inhibition regime (with no granule spike) where the network oscillates in the gamma (40-90Hz) band 2 - a strong inhibition regime (with granule spikes) where the network oscillates in the beta (15-30Hz) band. Slow modulations of sensory and centrifugal inputs, phase shifted by a quarter of cycle, possibly combined with short term depression of the mitral to granule AMPA synapse, allows the network to alternate between the two regimes as observed in anesthetized animals.
152.	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. ..."
153.	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.
154.	Global and multiplexed dendritic computations under in vivo-like conditions (Ujfalussy et al 2018)
		"The input-output transformation of neurons under in vivo conditions is unknown. Ujfalussy et al. use a model-based approach to show that linear integration with a single global dendritic nonlinearity can accurately predict the response of neurons to naturalistic synaptic input patterns."
155.	Globus pallidus neuron models with differing dendritic Na channel expression (Edgerton et al., 2010)
		A set of 9 multi-compartmental rat GP neuron models (585 compartments) differing only in their expression of dendritic fast sodium channels were compared in their synaptic integration properties. Dendritic fast sodium channels were found to increase the importance of distal synapses (both excitatory AND inhibitory), increase spike timing variability with in vivo-like synaptic input, and make the model neurons highly sensitive to clustered synchronous excitation.
156.	Glutamate diffusion and AMPA receptor activation in the cerebellar glomerulus (Saftenku 2005)
		Synaptic conductances are influenced markedly by the geometry of the space surrounding the synapse since the transient glutamate concentration in the synaptic cleft is determined by this geometry. Our paper is an attempt to understand the reasons for slow glutamate diffusion in the cerebellar glomerulus, a structure situated around the enlarged mossy fiber terminal in the cerebellum and surrounded by a glial sheath. ... Our results suggest at least a 7- to 10-fold lower apparent diffusion coefficient of glutamate in the porous medium of the glomerulus than in water. ... See paper for details and more.
157.	Granule Cells of the Olfactory Bulb (Simoes_De_Souza et al. 2014)
		Electrical responses of three classes of granule cells of the olfactory bulb to synaptic activation in different dendritic locations. The constructed models were based on morphological detailed compartmental reconstructions of three granule cell classes of the olfactory bulb with active dendrites described by Bhalla and Bower (J. Neurophysiol. 69: 1948-1965, 1993) and dendritic spine distributions described by Woolf et al. (J. Neurosci. 11: 1837-1854, 1991). The computational studies with the model neurons showed that different quantities of spines have to be activated in each dendritic region to induce an action potential, which always was originated in the active terminal dendrites, independently of the location of the stimuli and the morphology of the dendritic tree.
158.	H-currents effect on the fluctuation of gamma/beta oscillations (Avella-Gonzalez et al., 2015)
		This model was designed to study the impact of H-currents on the dynamics of cortical oscillations, and in paticular on the occurrence of high and low amplitude episodes (HAE, LAE) in network oscillations. The H-current is a slow, hyperpolarization-activated, depolarizing current that contributes to neuronal resonance and membrane potential. We characterized amplitude fluctuations in network oscillations by measuring the average durations of HAEs and LAEs, and explored how these were modulated by trains of external spikes, both in the presence and absence of H-channels. We looked at HAE duration, the frequency and power of network oscillations, and the effect of H-channels on the temporal voltage profile in single cells. We found that H-currents increased the oscillation frequency and, in combination with external spikes, representing input from areas outside the network, strongly decreased the synchrony of firing. As a consequence, the oscillation power and the duration of episodes during which the network exhibited high-amplitude oscillations were greatly reduced in the presence of H-channels.
159.	Heterosynaptic Spike-Timing-Dependent Plasticity (Hiratani & Fukai 2017)
		"The balance between excitatory and inhibitory inputs is a key feature of cortical dynamics. Such a balance is arguably preserved in dendritic branches, yet its underlying mechanism and functional roles remain unknown. In this study, we developed computational models of heterosynaptic spike-timing-dependent plasticity (STDP) to show that the excitatory/inhibitory balance in dendritic branches is robustly achieved through heterosynaptic interactions between excitatory and inhibitory synapses. The model reproduces key features of experimental heterosynaptic STDP well, and provides analytical insights. ..."
160.	Hierarchical network model of perceptual decision making (Wimmer et al 2015)
		Neuronal variability in sensory cortex predicts perceptual decisions. To investigate the interaction of bottom-up and top-down mechanisms during the decision process, we developed a hierarchical network model. The network consists of two circuits composed of leaky integrate-and-fire neurons: an integration circuit (e.g. LIP, FEF) and a sensory circuit (MT), recurrently coupled via bottom-up feedforward connections and top-down feedback connections. The integration circuit accumulates sensory evidence and produces a binary categorization due to winner-take-all competition between two decision-encoding populations (X.J. Wang, Neuron, 2002). The sensory circuit is a balanced randomly connected EI-network, that contains neural populations selective to opposite directions of motion. We have used this model to simulate a standard two-alternative forced-choice motion discrimination task.
161.	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."
162.	Hippocampal CA1 microcircuit model including somatic and dendritic inhibition
		Here, we investigate the role of (dis)inhibition on the lateral entorhinal cortex (LEC) induced dendritic spikes on hippocampal CA1 pyramidal cells. The circuit model consists of pyramidal, SST+, CCK+, CR+/VIP+, and CCK+/VIP+ cells.
163.	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).
164.	Hippocampal CA3 thorny and a-thorny principal neuron models (Linaro et al in review)
		This repository contains two populations of biophysically detailed models of murine hippocampal CA3 pyramidal neurons based on the two principal cell types that comprise this region. They are the result of a data-driven approach aimed at optimizing the model parameters by utilizing high-resolution morphological reconstructions and patch-clamp electrophysiology data together with a multi-objective optimization algorithm. The models quantitatively match the cell type-specific firing phenotypes and recapitulate the intrinsic population-level variability observed in the data. Additionally, the conductance values found by the optimization algorithm are consistent with differentially expressed ion channel genes in single-cell transcriptomic data for the two cell types. The models have further been employed to investigate the cell type-specific biophysical properties involved in the generation of complex-spiking output driven by synaptic input and to show that a-thorny bursting cells are capable of encoding more information in their firing output than their counterparts, thorny regular spiking neurons. Reference: Linaro D, Levy MJ, and Hunt, DL. Cell type-specific mechanisms of information transfer in data-driven biophysical models of hippocampal CA3 principal neurons. (2022) PLOS Computational Biology
165.	Hippocampal Mossy Fiber bouton: presynaptic KV7 channel function (Martinello et al 2019)
166.	Hippocampus temporo-septal engram shift model (Lytton 1999)
		Temporo-septal engram shift model of hippocampal memory. The model posits that memories gradually move along the hippocampus from a temporal encoding site to ever more septal sites from which they are recalled. We propose that the sense of time is encoded by the location of the engram along the temporo-septal axis.
167.	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."
168.	Homosynaptic plasticity in the tail withdrawal circuit (TWC) of Aplysia (Baxter and Byrne 2006)
		The tail-withdrawal circuit of Aplysia provides a useful model system for investigating synaptic dynamics. Sensory neurons within the circuit manifest several forms of synaptic plasticity. Here, we developed a model of the circuit and investigated the ways in which depression (DEP) and potentiation (POT) contributed to information processing. DEP limited the amount of motor neuron activity that could be elicited by the monosynaptic pathway alone. POT within the monosynaptic pathway did not compensate for DEP. There was, however, a synergistic interaction between POT and the polysynaptic pathway. This synergism extended the dynamic range of the network, and the interplay between DEP and POT made the circuit respond preferentially to long-duration, low-frequency inputs.
169.	Hopfield and Brody model (Hopfield, Brody 2000)
		NEURON implementation of the Hopfield and Brody model from the papers: JJ Hopfield and CD Brody (2000) JJ Hopfield and CD Brody (2001). Instructions are provided in the below readme.txt file.
170.	Human L5 Cortical Circuit (Guet-McCreight)
		We used L5 Pyr neuron models fit to electrophysiology data from younger and older individuals to simulate detailed human layer 5 microcircuits. These circuits also included detailed parvalbumin+ (PV), somatostatin+ (SST), and vasoactivate intestinal polypeptide+ (VIP) inhibitory interneuron models.
171.	Human layer 2/3 cortical microcircuits in health and depression (Yao et al, 2022)
172.	Human tactile FA1 neurons (Hay and Pruszynski 2020)
		"... we show that synaptic integration across the complex signals from the first-order neuronal population could underlie human ability to accurately (< 3°) and rapidly process the orientation of edges moving across the fingertip. We first derive spiking models of human first-order tactile neurons that fit and predict responses to moving edges with high accuracy. We then use the model neurons in simulating the peripheral neuronal population that innervates a fingertip. We train classifiers performing synaptic integration across the neuronal population activity, and show that synaptic integration across first-order neurons can process edge orientations with high acuity and speed. ... our models suggest that integration of fast-decaying (AMPA-like) synaptic inputs within short timescales is critical for discriminating fine orientations, whereas integration of slow-decaying (NMDA-like) synaptic inputs supports discrimination of coarser orientations and maintains robustness over longer timescales"
173.	Hybrid oscillatory interference / continuous attractor NN of grid cell firing (Bush & Burgess 2014)
		Matlab code to simulate a hybrid oscillatory interference - continuous attractor network model of grid cell firing in pyramidal and stellate cells of rodent medial entorhinal cortex
174.	I&F recurrent networks with current- or conductance-based synapses (Cavallari et al. 2014)
		Recurrent networks of two populations (excitatory and inhibitory) of randomly connected Leaky Integrate-and-Fire (LIF) neurons with either current- or conductance-based synapses from the paper S. Cavallari, S. Panzeri and A. Mazzoni (2014)
175.	Ih levels roles in bursting and regular-spiking subiculum pyramidal neurons (van Welie et al 2006)
		Pyramidal neurons in the subiculum typically display either bursting or regular-spiking behavior. ... Here we report that bursting neurons posses a hyperpolarization-activated cation current (Ih) that is two-fold larger (conductance: 5.3 ± 0.5 nS) than in regularspiking neurons (2.2 ± 0.6 nS), while Ih exhibits similar voltage-dependent and kinetic properties in both classes of neurons. Bursting and regular-spiking neurons display similar morphology. The difference in Ih between the two classes is not responsible for the distinct firing patterns, since neither pharmacological blockade of Ih nor enhancement of Ih using a dynamic clamp affects the qualitative firing patterns. Instead, the difference in Ih between bursting and regular-spiking neurons determines the temporal integration of evoked synaptic input from the CA1 area. In response to 50 Hz stimulation, bursting neurons, with a large Ih, show ~50% less temporal summation than regular-spiking neurons. ... A computer simulation model of a subicular neuron with the properties of either a bursting or a regular-spiking neuron confirmed the pivotal role of Ih in temporal integration of synaptic input. These data suggest that in the subicular network, bursting neurons are better suited to discriminate the content of high frequency input, such as that occurring during gamma oscillations, compared to regular-spiking neurons. See paper for more and details.
176.	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. ..."
177.	Impact of dendritic atrophy on intrinsic and synaptic excitability (Narayanan & Chattarji, 2010)
		These simulations examined the atrophy induced changes in electrophysiological properties of CA3 pyramidal neurons. We found these neurons change from bursting to regular spiking as atrophy increases. Region-specific atrophy induced region-specific increases in synaptic excitability in a passive dendritic tree. All dendritic compartments of an atrophied neuron had greater synaptic excitability and a larger voltage transfer to the soma than the control neuron.
178.	In silico hippocampal modeling for multi-target pharmacotherapy in schizophrenia (Sherif et al 2020)
		"Using a hippocampal CA3 computer model with 1200 neurons, we examined the effects of alterations in NMDAR, HCN (Ih current), and GABAAR on information flow (measured with normalized transfer entropy), and in gamma activity in local field potential (LFP). We found that altering NMDARs, GABAAR, Ih, individually or in combination, modified information flow in an inverted-U shape manner, with information flow reduced at low and high levels of these parameters. Theta-gamma phase-amplitude coupling also had an inverted-U shape relationship with NMDAR augmentation. The strong information flow was associated with an intermediate level of synchrony, seen as an intermediate level of gamma activity in the LFP, and an intermediate level of pyramidal cell excitability"
179.	In vivo imaging of dentate gyrus mossy cells in behaving mice (Danielson et al 2017)
		Mossy cells in the hilus of the dentate gyrus constitute a major excitatory principal cell type in the mammalian hippocampus, however, it remains unknown how these cells behave in vivo. Here, we have used two-photon Ca2+ imaging to monitor the activity of mossy cells in awake, behaving mice. We find that mossy cells are significantly more active than dentate granule cells in vivo, exhibit significant spatial tuning during head-fixed spatial navigation, and undergo robust remapping of their spatial representations in response to contextual manipulation. Our results provide the first characterization of mossy cells in the behaving animal and demonstrate their active participation in spatial coding and contextual representation.
180.	Initiation of spreading depolarization by GABAergic neuron hyperactivity & NaV 1.1 (Chever et al 21)
		Experimentally, we show that acute pharmacological activation of NaV1.1 (the main Na+ channel of interneurons) or optogenetic-induced hyperactivity of GABAergic interneurons is sufficient to ignite CSD in the neocortex by spiking-generated extracellular K+ build-up. Neither GABAergic nor glutamatergic synaptic transmission were required for CSD initiation. CSD was not generated in other brain areas, suggesting that this is a neocortex-specific mechanism of CSD initiation. Gain-of-function mutations of NaV1.1 (SCN1A) cause Familial Hemiplegic Migraine type-3 (FHM3), a subtype of migraine with aura, of which CSD is the neurophysiological correlate. Our results provide the mechanism linking NaV1.1 gain-of-function to CSD generation in FHM3. Those findings are supported by the two-neuron conductance-based model with dynamic ion concentrations we developed.
181.	Interacting synaptic conductances during, distorting, voltage clamp (Poleg-Polsky and Diamond 2011)
		This simulation examines the accuracy of the voltage clamp technique in detecting the excitatory and the inhibitory components of the synaptic drive.
182.	Intracortical synaptic potential modulation by presynaptic somatic potential (Shu et al. 2006, 2007)
		" ... Here we show that the voltage fluctuations associated with dendrosomatic synaptic activity propagate significant distances along the axon, and that modest changes in the somatic membrane potential of the presynaptic neuron modulate the amplitude and duration of axonal action potentials and, through a Ca21- dependent mechanism, the average amplitude of the postsynaptic potential evoked by these spikes. These results indicate that synaptic activity in the dendrite and soma controls not only the pattern of action potentials generated, but also the amplitude of the synaptic potentials that these action potentials initiate in local cortical circuits, resulting in synaptic transmission that is a mixture of triggered and graded (analogue) signals."
183.	Irregular spiking in NMDA-driven prefrontal cortex neurons (Durstewitz and Gabriel 2006)
		Slow N-Methyl-D-aspartic acid (NMDA) synaptic currents are assumed to strongly contribute to the persistently elevated firing rates observed in prefrontal cortex (PFC) during working memory. During persistent activity, spiking of many neurons is highly irregular. ... The highest interspike-interval (ISI) variability occurred in a transition regime where the subthreshold membrane potential distribution shifts from mono- to bimodality, ... Predictability within irregular ISI series was significantly higher than expected from a noise-driven linear process, indicating that it might best be described through complex (potentially chaotic) nonlinear deterministic processes. Accordingly, the phenomena observed in vitro could be reproduced in purely deterministic biophysical model neurons. High spiking irregularity in these models emerged within a chaotic, close-to-bifurcation regime characterized by a shift of the membrane potential distribution from mono- to bimodality and by similar ISI return maps as observed in vitro. ... NMDA-induced irregular dynamics may have important implications for computational processes during working memory and neural coding.
184.	Kinetic synaptic models applicable to building networks (Destexhe et al 1998)
		Simplified AMPA, NMDA, GABAA, and GABAB receptor models useful for building networks are described in a book chapter. One reference paper synthesizes a comprehensive general description of synaptic transmission with Markov kinetic models which is applicable to modeling ion channels, synaptic release, and all receptors. Also a simple introduction to this method is given in a seperate paper Destexhe et al Neural Comput 6:14-18 , 1994). More information and papers at http://cns.iaf.cnrs-gif.fr/Main.html and through email: Destexhe@iaf.cnrs-gif.fr
185.	KInNeSS : a modular framework for computational neuroscience (Versace et al. 2008)
		The xml files provided here implement a network of excitatory and inhibitory spiking neurons, governed by either Hodgkin-Huxley or quadratic integrate-and-fire dynamical equations. The code is used to demonstrate the capabilities of the KInNeSS software package for simulation of networks of spiking neurons. The simulation protocol used here is meant to facilitate the comparison of KInNeSS with other simulators reviewed in <a href="http://dx.doi.org/10.1007/s10827-007-0038-6">Brette et al. (2007)</a>. See the associated paper "Versace et al. (2008) KInNeSS : a modular framework for computational neuroscience." for an extensive description of KInNeSS .
186.	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...."
187.	KV1 channel governs cerebellar output to thalamus (Ovsepian et al. 2013)
		The output of the cerebellum to the motor axis of the central nervous system is orchestrated mainly by synaptic inputs and intrinsic pacemaker activity of deep cerebellar nuclear (DCN) projection neurons. Herein, we demonstrate that the soma of these cells is enriched with KV1 channels produced by mandatory multi-merization of KV1.1, 1.2 alpha andKV beta2 subunits. Being constitutively active, the K+ current (IKV1) mediated by these channels stabilizes the rate and regulates the temporal precision of self-sustained firing of these neurons. ... Through the use of multi-compartmental modelling and ... the physiological significance of the described functions for processing and communication of information from the lateral DCN to thalamic relay nuclei is established.
188.	L5 PFC microcircuit used to study persistent activity (Papoutsi et al. 2014, 2013)
		Using a heavily constrained biophysical model of a L5 PFC microcircuit we investigate the mechanisms that underlie persistent activity emergence (ON) and termination (OFF) and search for the minimum network size required for expressing these states within physiological regimes.
189.	L5 PFC pyramidal neurons (Papoutsi et al. 2017)
		" ... Here, we use a modeling approach to investigate whether and how the morphology of the basal tree mediates the functional output of neurons. We implemented 57 basal tree morphologies of layer 5 prefrontal pyramidal neurons of the rat and identified morphological types which were characterized by different response features, forming distinct functional types. These types were robust to a wide range of manipulations (distribution of active ionic mechanisms, NMDA conductance, somatic and apical tree morphology or the number of activated synapses) and supported different temporal coding schemes at both the single neuron and the microcircuit level. We predict that the basal tree morphological diversity among neurons of the same class mediates their segregation into distinct functional pathways. ..."
190.	L5 pyr. cell spiking control by oscillatory inhibition in distal apical dendrites (Li et al 2013)
		This model examined how distal oscillatory inhibition influences the firing of a biophysically-detailed layer 5 pyramidal neuron model.
191.	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.
192.	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. ..."
193.	Large-scale model of neocortical slice in vitro exhibiting persistent gamma (Tomsett et al. 2014)
		This model contains 15 neuron populations (8 excitatory, 7 inhibitory) arranged into 4 cortical layers (layer 1 empty, layers 2/3 combined). It produces a persistent gamma oscillation driven by layer 2/3. It runs using the VERTEX simulator, which is written in Matlab and is available from http://www.vertexsimulator.org
194.	Lateral dendrodenditic inhibition in the Olfactory Bulb (David et al. 2008)
		Mitral cells, the principal output neurons of the olfactory bulb, receive direct synaptic activation from primary sensory neurons. Shunting inhibitory inputs delivered by granule cell interneurons onto mitral cell lateral dendrites are believed to influence spike timing and underlie coordinated field potential oscillations. Lateral dendritic shunt conductances delayed spiking to a degree dependent on both their electrotonic distance and phase of onset. Recurrent inhibition significantly narrowed the distribution of mitral cell spike times, illustrating a tendency towards coordinated synchronous activity. This result suggests an essential role for early mechanisms of temporal coordination in olfaction. The model was adapted from Davison et al, 2003, but include additional noise mechanisms, long lateral dendrite, and specific synaptic point processes.
195.	Layer V PFC pyramidal neuron used to study persistent activity (Sidiropoulou & Poirazi 2012)
		"... Here, we use a compartmental modeling approach to search for discriminatory features in the properties of incoming stimuli to a PFC pyramidal neuron and/or its response that signal which of these stimuli will result in persistent activity emergence. Furthermore, we use our modeling approach to study cell-type specific differences in persistent activity properties, via implementing a regular spiking (RS) and an intrinsic bursting (IB) model neuron. ... Collectively, our results pinpoint to specific features of the neuronal response to a given stimulus that code for its ability to induce persistent activity and predict differential roles of RS and IB neurons in persistent activity expression. "
196.	Layer V pyramidal cell functions and schizophrenia genetics (Mäki-Marttunen et al 2019)
		Study on how GWAS-identified risk genes of shizophrenia affect excitability and integration of inputs in thick-tufted layer V pyramidal cells
197.	Leaky Integrate and Fire Neuron Model of Context Integration (Calvin, Redish 2021)
		The maintenance of the contextual information has been shown to be sensitive to changes in excitation-inhbition (EI) balance. We constructed a multi-structure, biophysically-realistic agent that could perform context-integration as is assessed by the dot probe expectancy task. The agent included a perceptual network, a working memory network, and a decision making system and was capable of successfully performing the dot probe expectancy task. Systemic manipulation of the agent’s EI balance produced localized dysfunction of the memory structure, which resulted in schizophrenia-like deficits at context integration.
198.	Learning intrinsic excitability in Medium Spiny Neurons (Scheler 2014)
		"We present an unsupervised, local activation-dependent learning rule for intrinsic plasticity (IP) which affects the composition of ion channel conductances for single neurons in a use-dependent way. We use a single-compartment conductance-based model for medium spiny striatal neurons in order to show the effects of parameterization of individual ion channels on the neuronal membrane potential-curent relationship (activation function). We show that parameter changes within the physiological ranges are sufficient to create an ensemble of neurons with significantly different activation functions. ... "
199.	Learning spatial transformations through STDP (Davison, Frégnac 2006)
		A common problem in tasks involving the integration of spatial information from multiple senses, or in sensorimotor coordination, is that different modalities represent space in different frames of reference. Coordinate transformations between different reference frames are therefore required. One way to achieve this relies on the encoding of spatial information using population codes. The set of network responses to stimuli in different locations (tuning curves) constitute a basis set of functions which can be combined linearly through weighted synaptic connections in order to approximate non-linear transformations of the input variables. The question then arises how the appropriate synaptic connectivity is obtained. This model shows that a network of spiking neurons can learn the coordinate transformation from one frame of reference to another, with connectivity that develops continuously in an unsupervised manner, based only on the correlations available in the environment, and with a biologically-realistic plasticity mechanism (spike timing-dependent plasticity).
200.	Levodopa-Induced Toxicity in Parkinson's Disease (Muddapu et al, 2022)
		"... We present a systems-level computational model of SNc-striatum, which will help us understand the mechanism behind neurodegeneration postulated above and provide insights into developing disease-modifying therapeutics. It was observed that SNc terminals are more vulnerable to energy deficiency than SNc somas. During L-DOPA therapy, it was observed that higher L-DOPA dosage results in increased loss of terminals in SNc. It was also observed that co-administration of L-DOPA and glutathione (antioxidant) evades L-DOPA-induced toxicity in SNc neurons. Our proposed model of the SNc-striatum system is the first of its kind, where SNc neurons were modeled at a biophysical level, and striatal neurons were modeled at a spiking level. We show that our proposed model was able to capture L-DOPA-induced toxicity in SNc, caused by energy deficiency."
201.	LFP in striatum (Tanaka & Nakamura 2019)
		The numerical simulations of LFP generation by cortical pyramidal neuron and medium-sized spiny neurons.
202.	LIP and FEF rhythmic attention model (Aussel et al. 2023)
		This model investigates how theta-rhythmic performance in an attentional task can emerge from the dynamics of the Lateral IntraParietal area (LIP) and the Frontal Eye Fields (FEF) when stimulated by the medial-dorsal pulvinar.
203.	Locus Coeruleus blocking model (Chowdhury et al.)
		"... Here, we show that Locus Coeruleus (LC) cells projecting to dCA1 have a key permissive role in contextual memory linking, without affecting contextual memory formation, and that this effect is mediated by dopamine. Additionally, we found that LC to dCA1 projecting neurons modulate the excitability of dCA1 neurons, and the extent of overlap between dCA1 memory ensembles, as well as the stability of coactivity patterns within these ensembles..."
204.	Long time windows from theta modulated inhib. in entorhinal–hippo. loop (Cutsuridis & Poirazi 2015)
		"A recent experimental study (Mizuseki et al., 2009) has shown that the temporal delays between population activities in successive entorhinal and hippocampal anatomical stages are longer (about 70–80 ms) than expected from axon conduction velocities and passive synaptic integration of feed-forward excitatory inputs. We investigate via computer simulations the mechanisms that give rise to such long temporal delays in the hippocampus structures. ... The model shows that the experimentally reported long temporal delays in the DG, CA3 and CA1 hippocampal regions are due to theta modulated somatic and axonic inhibition..."
205.	Look-Up Table Synapse (LUTsyn) models for AMPA and NMDA (Pham et al., 2021)
		Fast input-output synapse model of glutamatergic receptors AMPA and NMDA that can capture nonlinear interactions via look-up table abstraction. Speeds are comparable to 'linear' exponential synapses. Download LUT files at: https://senselab.med.yale.edu/modeldb/data/267103/LUTs.zip
206.	LTP in cerebellar mossy fiber-granule cell synapses (Saftenku 2002)
		We simulated synaptic transmission and modified a simple model of long-term potentiation (LTP) and long-term depression (LTD) in order to describe long-term plasticity related changes in cerebellar mossy fiber-granule cell synapses. In our model, protein autophosphorylation, leading to the maintenance of long-term plasticity, is controlled by Ca2+ entry through the NMDA receptor channels. The observed nonlinearity in the development of long-term changes of EPSP in granule cells is explained by the difference in the rate constants of two independent autocatalytic processes.
207.	Mathematical model for windup (Aguiar et al. 2010)
		"Windup is characterized as a frequency-dependent increase in the number of evoked action potentials in dorsal horn neurons in response to electrical stimulation of afferent C-fibers. ... The approach presented here relies on mathematical and computational analysis to study the mechanism(s) underlying windup. From experimentally obtained windup profiles, we extract the time scale of the facilitation mechanisms that may support the characteristics of windup. Guided by these values and using simulations of a biologically realistic compartmental model of a wide dynamic range (WDR) neuron, we are able to assess the contribution of each mechanism for the generation of action potentials windup. ..."
208.	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.
209.	Mechanisms underlying subunit independence in pyramidal neuron dendrites (Behabadi and Mel 2014)
		"...Using a detailed compartmental model of a layer 5 pyramidal neuron, and an improved method for quantifying subunit independence that incorporates a more accurate model of dendritic integration, we first established that the output of each dendrite can be almost perfectly predicted by the intensity and spatial configuration of its own synaptic inputs, and is nearly invariant to the rate of bAP-mediated 'cross-talk' from other dendrites over a 100-fold range..."
210.	MEG of Somatosensory Neocortex (Jones et al. 2007)
		"... To make a direct and principled connection between the SI (somatosensory primary neocortex magnetoencephalography) waveform and underlying neural dynamics, we developed a biophysically realistic computational SI model that contained excitatory and inhibitory neurons in supragranular and infragranular layers. ... our model provides a biophysically realistic solution to the MEG signal and can predict the electrophysiological correlates of human perception."
211.	Microcircuits of L5 thick tufted pyramidal cells (Hay & Segev 2015)
		"... We simulated detailed conductance-based models of TTCs (Layer 5 thick tufted pyramidal cells) forming recurrent microcircuits that were interconnected as found experimentally; the network was embedded in a realistic background synaptic activity. ... Our findings indicate that dendritic nonlinearities are pivotal in controlling the gain and the computational functions of TTCs microcircuits, which serve as a dominant output source for the neocortex. "
212.	Mirror Neuron (Antunes et al 2017)
		Modeling Mirror Neurons Through Spike-Timing Dependent Plasticity. This script reproduces Figure 3B.
213.	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. ..."
214.	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."
215.	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
216.	Modeling dentate granule cells heterosynaptic plasticity using STDP-BCM rule (Jedlicka et al. 2015)
		... Here we study how key components of learning mechanisms in the brain, namely spike timing-dependent plasticity and metaplasticity, interact with spontaneous activity in the input pathways of the neuron. Using biologically realistic simulations we show that ongoing background activity is a key determinant of the degree of long-term potentiation and long-term depression of synaptic transmission between nerve cells in the hippocampus of freely moving animals. This work helps better understand the computational rules which drive synaptic plasticity in vivo. ...
217.	Modeling temperature changes in AMPAR kinetics (Postlethwaite et al 2007)
		This model was used to simulate glutamatergic, AMPA receptor mediated mEPSCs (miniature EPSCs, resulting from spontaneous vesicular transmitter release) at the calyx of Held synapse. It was used to assess the influence of temperature (physiological vs. subphysiological) on the amplitude and time course of mEPSCs. In the related paper, simulation results were directly compared to the experimental data, and it was concluded that an increase of temperature accelerates AMPA receptor kinetics.
218.	Modelling platform of the cochlear nucleus and other auditory circuits (Manis & Compagnola 2018)
		"Models of the auditory brainstem have been an invaluable tool for testing hypotheses about auditory information processing and for highlighting the most important gaps in the experimental literature. Due to the complexity of the auditory brainstem, and indeed most brain circuits, the dynamic behavior of the system may be difficult to predict without a detailed, biologically realistic computational model. Despite the sensitivity of models to their exact construction and parameters, most prior models of the cochlear nucleus have incorporated only a small subset of the known biological properties. This confounds the interpretation of modelling results and also limits the potential future uses of these models, which require a large effort to develop. To address these issues, we have developed a general purpose, bio-physically detailed model of the cochlear nucleus for use both in testing hypotheses about cochlear nucleus function and also as an input to models of downstream auditory nuclei. The model implements conductance-based Hodgkin-Huxley representations of cells using a Python-based interface to the NEURON simulator. ..."
219.	Modulation of temporal integration window (Migliore, Shepherd 2002)
		Model simulation file from the paper M.Migliore and Gordon M. Shepherd Emerging rules for distributions of active dendritic properties underlying specific neuronal functions. Nature Rev. Neurosci. 3, 362-370 (2002).
220.	Molecular layer interneurons in cerebellum encode valence in associative learning (Ma et al 2020)
		We used two-photon microscopy to study the role of ensembles of cerebellar molecular layer interneurons (MLIs) in a go-no go task where mice obtain a sugar water reward. In order to begin understanding the circuit basis of our findings in changes in lick behavior with chemogenetics in the go-no go associative learning olfactory discrimination task we generated a simple computational model of MLI interaction with PCs.
221.	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."
222.	Motor system model with reinforcement learning drives virtual arm (Dura-Bernal et al 2017)
		"We implemented a model of the motor system with the following components: dorsal premotor cortex (PMd), primary motor cortex (M1), spinal cord and musculoskeletal arm (Figure 1). PMd modulated M1 to select the target to reach, M1 excited the descending spinal cord neurons that drove the arm muscles, and received arm proprioceptive feedback (information about the arm position) via the ascending spinal cord neurons. The large-scale model of M1 consisted of 6,208 spiking Izhikevich model neurons [37] of four types: regular-firing and bursting pyramidal neurons, and fast-spiking and low-threshold-spiking interneurons. These were distributed across cortical layers 2/3, 5A, 5B and 6, with cell properties, proportions, locations, connectivity, weights and delays drawn primarily from mammalian experimental data [38], [39], and described in detail in previous work [29]. The network included 486,491 connections, with synapses modeling properties of four different receptors ..."
223.	Multiple mechanisms of short term plasticity at the calyx of Held (Hennig et al. 2008)
		This is a new model of the short-term dynamics of glutamatergic synaptic transmission, which incorporates multiple mechanisms acting at differing sites and across a range of different time scales (ms to tens of seconds). In the paper, we show that this model can accurately reproduce the experimentally measured time-course of short term depression across different stimulus frequencies at the calyx of Held. The model demonstrates how multiple forms of activity-dependent modulation of release probability and vesicle pool depletion interact, and shows how stimulus-history-dependent recovery from synaptic depression can arise from dynamics on multiple time scales.
224.	Multiplication by NMDA receptors in Direction Selective Ganglion cells (Poleg-Polsky & Diamond 2016)
		The model demonstrates how signal amplification with NMDARs depends on the synaptic environment. When direction selectivity (DS) detection is mediated by DS inhibition, NMDARs multiply other synaptic conductances. In the case of DS tuned excitation, NMDARs contribute additively.
225.	Multiscale interactions between chemical and electric signaling in LTP (Bhalla 2011)
		"Synaptic plasticity leads to long-term changes in excitability, whereas cellular homeostasis maintains excitability. Both these processes involve interactions between molecular events, electrical events, and network activity. Here I explore these intersections with a multilevel model that embeds molecular events following synaptic calcium influx into a multicompartmental electrical model of a CA1 hippocampal neuron. ..."
226.	Multiscale model of excitotoxicity in PD (Muddapu and Chakravarthy 2020)
		Parkinson's disease (PD) is a neurodegenerative disorder caused by loss of dopaminergic neurons in Substantia Nigra pars compacta (SNc). Although the exact cause of cell death is not clear, the hypothesis that metabolic deficiency is a key factor has been gaining attention in recent years. In the present study, we investigate this hypothesis using a multi-scale computational model of the subsystem of the basal ganglia comprising Subthalamic Nucleus (STN), Globus Pallidus externa (GPe) and SNc. The proposed model is a multiscale model in that interactions among the three nuclei are simulated using more abstract Izhikevich neuron models, while the molecular pathways involved in cell death of SNc neurons are simulated in terms of detailed chemical kinetics. Simulation results obtained from the proposed model showed that energy deficiencies occurring at cellular and network levels could precipitate the excitotoxic loss of SNc neurons in PD. At the subcellular level, the models show how calcium elevation leads to apoptosis of SNc neurons. The therapeutic effects of several neuroprotective interventions are also simulated in the model. From neuroprotective studies, it was clear that glutamate inhibition and apoptotic signal blocker therapies were able to halt the progression of SNc cell loss when compared to other therapeutic interventions, which only slows down the progression of SNc cell loss.
227.	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. ..."
228.	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.
229.	Na channel mutations in the dentate gyrus (Thomas et al. 2009)
		These are source files to generate the data in Figure 6 from "Mossy fiber sprouting interacts with sodium channel mutations to increase dentate gyrus excitability" Thomas EA, Reid CA, Petrou S, Epilepsia (2009)
230.	NAcc medium spiny neuron: effects of cannabinoid withdrawal (Spiga et al. 2010)
		Cannabinoid withdrawal produces a hypofunction of dopaminergic neurons targeting medium spiny neurons (MSN) of the forebrain. Administration of a CB1 receptor antagonist to control rats provoked structural abnormalities, reminiscent of those observed in withdrawal conditions and support the regulatory role of cannabinoids in neurogenesis, axonal growth and synaptogenesis. Experimental observations were incorporated into a realistic computational model which predicts a strong reduction in the excitability of morphologically-altered MSN, yielding a significant reduction in action potential output. These paper provided direct morphological evidence for functional abnormalities associated with cannabinoid dependence at the level of dopaminergic neurons and their post synaptic counterpart, supporting a hypodopaminergic state as a distinctive feature of the “addicted brain”.
231.	Network bursts in cultured NN result from different adaptive mechanisms (Masquelier & Deco 2013)
		It is now well established that cultured neuron networks are spontaneously active, and tend to synchronize. Synchronous events typically involve the whole network, and have thus been termed “network spikes” (NS). Using experimental recordings and numerical simulations, we show here that the inter-NS interval statistics are complex, and allow inferring the neural mechanisms at work, in particular the adaptive ones, and estimating a number of parameters to which we cannot access experimentally.
232.	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.
233.	Network model with neocortical architecture (Anderson et al 2007,2012; Azhar et al 2012)
		Architecturally realistic neocortical model using seven classes of excitatory and inhibitory single compartment Hodgkin-Huxley cells. This is an addendum to ModelDB Accession # 98902, Studies of stimulus parameters for seizure disruption (Anderson et al. 2007). Wiring is adapted from the minicolumn hypothesis and incorporates visual and neocortical wiring data. Simulation demonstrates spontaneous bursting onset and cessation. This activity can be induced by random fluctuations in the surrounding background input.
234.	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. ..."
235.	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.
236.	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
237.	Neural mass model of the sleeping cortex (Weigenand et al 2014)
		Generates typical EEG data of sleeping Humans for sleep stages N2/N3 as well as wakefulness
238.	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.
239.	Neuronal dendrite calcium wave model (Neymotin et al, 2015)
		"... We developed a reaction-diffusion model of an apical dendrite with diffusible inositol triphosphate (IP3 ), diffusible Ca2+, IP3 receptors (IP3 Rs), endoplasmic reticulum (ER) Ca2+ leak, and ER pump (SERCA) on ER. ... At least two modes of Ca2+ wave spread have been suggested: a continuous mode based on presumed relative homogeneity of ER within the cell; and a pseudo-saltatory model where Ca2+ regeneration occurs at discrete points with diffusion between them. We compared the effects of three patterns of hypothesized IP3 R distribution: 1. continuous homogeneous ER, 2. hotspots with increased IP3R density (IP3 R hotspots), 3. areas of increased ER density (ER stacks). All three modes produced Ca2+ waves with velocities similar to those measured in vitro (~50 - 90µm /sec). ... The measures were sensitive to changes in density and spacing of IP3 R hotspots and stacks. ... An extended electrochemical model, including voltage gated calcium channels and AMPA synapses, demonstrated that membrane priming via AMPA stimulation enhances subsequent Ca2+ wave amplitude and duration. Our modeling suggests that pharmacological targeting of IP3 Rs and SERCA could allow modulation of Ca2+ wave propagation in diseases where Ca2+ dysregulation has been implicated. "
240.	Nigral dopaminergic neurons: effects of ethanol on Ih (Migliore et al. 2008)
		We use a realistic computational model of dopaminergic neurons in vivo to suggest that ethanol, through its effects on Ih, modifies the temporal structure of the spiking activity. The model predicts that the dopamine level may increase much more during bursting than pacemaking activity, especially in those brain regions with a slow dopamine clearance rate. The results suggest that a selective pharmacological remedy could thus be devised against the rewarding effects of ethanol that are postulated to mediate alcohol abuse and addiction, targeting the specific HCN genes expressed in dopaminergic neurons.
241.	NMDA receptors enhance the fidelity of synaptic integration (Li and Gulledge 2021)
		Excitatory synaptic transmission in many neurons is mediated by two co-expressed ionotropic glutamate receptor subtypes, AMPA and NMDA receptors, that differ in their kinetics, ion-selectivity, and voltage-sensitivity. AMPA receptors have fast kinetics and are voltage-insensitive, while NMDA receptors have slower kinetics and increased conductance at depolarized membrane potentials. Here we report that the voltage-dependency and kinetics of NMDA receptors act synergistically to stabilize synaptic integration of excitatory postsynaptic potentials (EPSPs) across spatial and voltage domains. Simulations of synaptic integration in simplified and morphologically realistic dendritic trees revealed that the combined presence of AMPA and NMDA conductances reduces the variability of somatic responses to spatiotemporal patterns of excitatory synaptic input presented at different initial membrane potentials and/or in different dendritic domains. This moderating effect of the NMDA conductance on synaptic integration was robust across a wide range of AMPA-to-NMDA ratios, and results from synergistic interaction of NMDA kinetics (which reduces variability across membrane potential) and voltage-dependence (which favors stabilization across dendritic location). When combined with AMPA conductance, the NMDA conductance balances voltage- and impedance-dependent changes in synaptic driving force, and distance-dependent attenuation of synaptic potentials arriving at the axon, to increase the fidelity of synaptic integration and EPSP-spike coupling across neuron state (i.e., initial membrane potential) and dendritic location of synaptic input. Thus, synaptic NMDA receptors convey advantages for synaptic integration that are independent of, but fully compatible with, their importance for coincidence detection and synaptic plasticity.
242.	NMDA spikes in basal dendrites of L5 pyramidal neurons (Polsky et al. 2009)
		"... In apical dendrites of neocortical pyramidal neurons, calcium spikes are known to contribute to burst generation, but a comparable understanding of basal dendritic mechanisms is lacking. Here we show that NMDA spikes in basal dendrites mediate both detection and generation of bursts through a postsynaptic mechanism. High-frequency inputs to basal dendrites markedly facilitated NMDA spike initiation compared with low-frequency activation or single inputs. ..."
243.	NMDAR & GABAB/KIR Give Bistable Dendrites: Working Memory & Sequence Readout (Sanders et al., 2013)
		" ...Here, we show that the voltage dependence of the inwardly rectifying potassium (KIR) conductance activated by GABA(B) receptors adds substantial robustness to network simulations of bistability and the persistent firing that it underlies. The hyperpolarized state is robust because, at hyperpolarized potentials, the GABA(B)/KIR conductance is high and the NMDA conductance is low; the depolarized state is robust because, at depolarized potentials, the NMDA conductance is high and the GABA(B)/KIR conductance is low. Our results suggest that this complementary voltage dependence of GABA(B)/KIR and NMDA conductances makes them a "perfect couple" for producing voltage bistability."
244.	Nonlinear dendritic processing in barrel cortex spiny stellate neurons (Lavzin et al. 2012)
		This is a multi-compartmental simulation of a spiny stellate neuron which is stimulated by a thalamocortical (TC) and cortico-cortical (CC) inputs. No other cells are explicitly modeled; the presynaptic network activation is represented by the number of active synapses. Preferred and non –preferred thalamic directions thus correspond to larder/smaller number of TC synapses. This simulation revealed that randomly activated synapses can cooperatively trigger global NMDA spikes, which involve participation of most of the dendritic tree. Surprisingly, we found that although the voltage profile of the cell was uniform, the calcium influx was restricted to ‘hot spots’ which correspond to synaptic clusters or large conductance synapses
245.	Numerical Integration of Izhikevich and HH model neurons (Stewart and Bair 2009)
		The Parker-Sochacki method is a new technique for the numerical integration of differential equations applicable to many neuronal models. Using this method, the solution order can be adapted according to the local conditions at each time step, enabling adaptive error control without changing the integration timestep. We apply the Parker-Sochacki method to the Izhikevich ‘simple’ model and a Hodgkin-Huxley type neuron, comparing the results with those obtained using the Runge-Kutta and Bulirsch-Stoer methods.
246.	Olfactory bulb cluster formation (Migliore et al. 2010)
		Functional roles of distributed synaptic clusters in the mitral-granule cell network of the olfactory bulb.
247.	Olfactory bulb granule cell: effects of odor deprivation (Saghatelyan et al 2005)
		The model supports the experimental findings on the effects of postnatal odor deprivation, and shows that a -10mV shift in the Na activation or a reduction in the dendritic length of newborn GC could independently explain the observed increase in excitability.
248.	Olfactory bulb microcircuits model with dual-layer inhibition (Gilra & Bhalla 2015)
		A detailed network model of the dual-layer dendro-dendritic inhibitory microcircuits in the rat olfactory bulb comprising compartmental mitral, granule and PG cells developed by Aditya Gilra, Upinder S. Bhalla (2015). All cell morphologies and network connections are in NeuroML v1.8.0. PG and granule cell channels and synapses are also in NeuroML v1.8.0. Mitral cell channels and synapses are in native python.
249.	Olfactory bulb mitral and granule cell column formation (Migliore et al. 2007)
		In the olfactory bulb, the processing units for odor discrimination are believed to involve dendrodendritic synaptic interactions between mitral and granule cells. There is increasing anatomical evidence that these cells are organized in columns, and that the columns processing a given odor are arranged in widely distributed arrays. Experimental evidence is lacking on the underlying learning mechanisms for how these columns and arrays are formed. We have used a simplified realistic circuit model to test the hypothesis that distributed connectivity can self-organize through an activity-dependent dendrodendritic synaptic mechanism. The results point to action potentials propagating in the mitral cell lateral dendrites as playing a critical role in this mechanism, and suggest a novel and robust learning mechanism for the development of distributed processing units in a cortical structure.
250.	Olfactory bulb mitral and granule cell: dendrodendritic microcircuits (Migliore and Shepherd 2008)
		This model shows how backpropagating action potentials in the long lateral dendrites of mitral cells, together with granule cell actions on mitral cells within narrow columns forming glomerular units, can provide a mechanism to activate strong local inhibition between arbitrarily distant mitral cells. The simulations predict a new role for the dendrodendritic synapses in the multicolumnar organization of the granule cells.
251.	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.
252.	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.
253.	Olfactory Computations in Mitral-Granule cell circuits (Migliore & McTavish 2013)
		Model files for the entry "Olfactory Computations in Mitral-Granule Cell Circuits" of the Springer Encyclopedia of Computational Neuroscience by Michele Migliore and Tom Mctavish. The simulations illustrate two typical Mitral-Granule cell circuits in the olfactory bulb of vertebrates: distance-independent lateral inhibition and gating effects.
254.	Olfactory Mitral cell: AP initiation modes (Chen et al 2002)
		The mitral cell primary dendrite plays an important role in transmitting distal olfactory nerve input from olfactory glomerulus to the soma-axon initial segment. To understand how dendritic active properties are involved in this transmission, we have combined dual soma and dendritic patch recordings with computational modeling to analyze action-potential initiation and propagation in the primary dendrite.
255.	Orientation preference in L23 V1 pyramidal neurons (Park et al 2019)
		"Pyramidal neurons integrate synaptic inputs from basal and apical dendrites to generate stimulus-specific responses. It has been proposed that feed-forward inputs to basal dendrites drive a neuron’s stimulus preference, while feedback inputs to apical dendrites sharpen selectivity. However, how a neuron’s dendritic domains relate to its functional selectivity has not been demonstrated experimentally. We performed 2-photon dendritic micro-dissection on layer-2/3 pyramidal neurons in mouse primary visual cortex. We found that removing the apical dendritic tuft did not alter orientation-tuning. Furthermore, orientation-tuning curves were remarkably robust to the removal of basal dendrites: ablation of 2 basal dendrites was needed to cause a small shift in orientation preference, without significantly altering tuning width. Computational modeling corroborated our results and put limits on how orientation preferences among basal dendrites differ in order to reproduce the post-ablation data. In conclusion, neuronal orientation-tuning appears remarkably robust to loss of dendritic input."
256.	Parallel odor processing by mitral and middle tufted cells in the OB (Cavarretta et al 2016, 2018)
		"[...] experimental findings suggest that MC and mTC may encode parallel and complementary odor representations. We have analyzed the functional roles of these pathways by using a morphologically and physiologically realistic three-dimensional model to explore the MC and mTC microcircuits in the glomerular layer and deeper plexiform layers. [...]"
257.	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.
258.	Perceptual judgments via sensory-motor interaction assisted by cortical GABA (Hoshino et al 2018)
		"Recurrent input to sensory cortex, via long-range reciprocal projections between motor and sensory cortices, is essential for accurate perceptual judgments. GABA levels in sensory cortices correlate with perceptual performance. We simulated a neuron-astrocyte network model to investigate how top-down, feedback signaling from a motor network (Nmot) to a sensory network (Nsen) affects perceptual judgments in association with ambient (extracellular) GABA levels. In the Nsen, astrocytic transporters modulated ambient GABA levels around pyramidal cells. A simple perceptual task was implemented: detection of a feature stimulus presented to the Nsen. ..."
259.	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.
260.	PKMZ synthesis and AMPAR regulation in late long-term synaptic potentiation (Helfer & Shultz 2018)
		Stochastic simulation of a set of molecular reactions that implement late long-term potentiation (L-LTP). The model is able to account for a wide range of empirical results, including induction and maintenance of late-phase LTP, cellular memory reconsolidation and the effects of different pharmaceutical interventions.
261.	Population models of temporal differentiation (Tripp and Eliasmith 2010)
		"Temporal derivatives are computed by a wide variety of neural circuits, but the problem of performing this computation accurately has received little theoretical study. Here we systematically compare the performance of diverse networks that calculate derivatives using cell-intrinsic adaptation and synaptic depression dynamics, feedforward network dynamics, and recurrent network dynamics. Examples of each type of network are compared by quantifying the errors they introduce into the calculation and their rejection of high-frequency input noise. ..."
262.	PreBotzinger Complex inspiratory neuron with NaP and CAN currents (Park and Rubin 2013)
		We have built on earlier models to develop a single-compartment Hodgkin-Huxley type model incorporating NaP and CAN currents, both of which can play important roles in bursting of inspiratory neurons in the PreBotzinger Complex of the mammalian respiratory brain stem. The model tracks the evolution of membrane potential, related (in)activation variables, calcium concentration, and available fraction of IP3 channels. The model can produce several types of bursting, presented and analyzed from a dynamical systems perspective in our paper.
263.	Prosthetic electrostimulation for information flow repair in a neocortical simulation (Kerr 2012)
		This model is an extension of a model ( http://modeldb.yale.edu/138379 ) recently published in Frontiers in Computational Neuroscience. This model consists of 4700 event-driven, rule-based neurons, wired according to anatomical data, and driven by both white-noise synaptic inputs and a sensory signal recorded from a rat thalamus. Its purpose is to explore the effects of cortical damage, along with the repair of this damage via a neuroprosthesis.
264.	Pyramidal neuron conductances state and STDP (Delgado et al. 2010)
		Neocortical neurons in vivo process each of their individual inputs in the context of ongoing synaptic background activity, produced by the thousands of presynaptic partners a typical neuron has. That background activity affects multiple aspects of neuronal and network function. However, its effect on the induction of spike-timing dependent plasticity (STDP) is not clear. Using the present biophysically-detailed computational model, it is not only able to replicate the conductance-dependent shunting of dendritic potentials (Delgado et al,2010), but show that synaptic background can truncate calcium dynamics within dendritic spines, in a way that affects potentiation more strongly than depression. This program uses a simplified layer 2/3 pyramidal neuron constructed in NEURON. It was similar to the model of Traub et al., J Neurophysiol. (2003), and consisted of a soma, an apical shaft, distal dendrites, five basal dendrites, an axon, and a single spine. The spine’s location was variable along the apical shaft (initial 50 &#956;m) and apical. The axon contained an axon hillock region, an initial segment, segments with myelin, and nodes of Ranvier, in order to have realistic action potential generation. For more information about the model see supplemental material, Delgado et al 2010.
265.	Pyramidal neuron, fast, regular, and irregular spiking interneurons (Konstantoudaki et al 2014)
		This is a model network of prefrontal cortical microcircuit based primarily on rodent data. It includes 16 pyramidal model neurons, 2 fast spiking interneuron models, 1 regular spiking interneuron model and 1 irregular spiking interneuron model. The goal of the paper was to use this model network to determine the role of specific interneuron subtypes in persistent activity
266.	Pyramidal Neuron: Deep, Thalamic Relay and Reticular, Interneuron (Destexhe et al 1998, 2001)
		This package shows single-compartment models of different classes of cortical neurons, such as the "regular-spiking", "fast-spiking" and "bursting" (LTS) neurons. The mechanisms included are the Na+ and K+ currents for generating action potentials (INa, IKd), the T-type calcium current (ICaT), and a slow voltage-dependent K+ current (IM). See http://cns.fmed.ulaval.ca/alain_demos.html
267.	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.
268.	Rat LGN Thalamocortical Neuron (Connelly et al 2015, 2016)
		" ... Here, combining data from fluorescence-targeted dendritic recordings and Ca2+ imaging from low-threshold spiking cells in rat brain slices with computational modeling, the cellular mechanism responsible for LTS (Low Threshold Spike) generation is established. ..." " ... Using dendritic recording, 2-photon glutamate uncaging, and computational modeling, we investigated how rat dorsal lateral geniculate nucleus thalamocortical neurons integrate excitatory corticothalamic feedback. ..."
269.	Reconstructed neuron (cerebellar, hippocampal, striatal) sims using predicted diameters (Reed et al)
		Many neuron morphologies in NeuroMorpho.org do not contain accurate dendritic diameters that are needed for simulations. We used a set of archives which did have realistic morphologies to derive equations predicting dendritic diameter, and to create morphologies using the predictions. The equations and new morphologies are derived by 1. extracting morphology features from swc files (morph_feature_extract.py) 2. using multiple regression to derive equations predicting diameter, (morph_feature_extract.py ) 3. using the equations to create the new morphology files from original swc file (shape_shifter.py). The python programs are all available from github.com/neurord/ShapeShifter We simulated the original morphologies and the morphologies with predicted diameter in Moose, evaluating the response to current injection and synaptic input. The code provided implements those simulations
270.	Reconstructing cerebellar granule layer evoked LFP using convolution (ReConv) (Diwakar et al. 2011)
		The model allows reconstruction of evoked local field potentials as seen in the cerebellar granular layer. The approach uses a detailed model of cerebellar granule neuron to generate data traces and then uses a "ReConv" or jittered repetitive convolution technique to reproduce post-synaptic local field potentials in the granular layer. The algorithm was used to generate both in vitro and in vivo evoked LFP and reflected the changes seen during LTP and LTD, when such changes were induced in the underlying neurons by modulating release probability of synapses and sodium channel regulated intrinsic excitability of the cells.
271.	Reducing variability in motor cortex activity by GABA (Hoshino et al. 2019)
		Interaction between sensory and motor cortices is crucial for perceptual decision-making, in which intracortical inhibition might have an important role. We simulated a neural network model consisting of a sensory network (NS) and a motor network (NM) to elucidate the significance of their interaction in perceptual decision-making in association with the level of GABA in extracellular space: extracellular GABA concentration. Extracellular GABA molecules acted on extrasynaptic receptors embedded in membranes of pyramidal cells and suppressed them. A reduction in extracellular GABA concentration either in NS or NM increased the rate of errors in perceptual decision-making, for which an increase in ongoing-spontaneous fluctuations in subthreshold neuronal activity in NM prior to sensory stimulation was responsible. Feedback (NM-to-NS) signaling enhanced selective neuronal responses in NS, which in turn increased stimulus-evoked neuronal activity in NM. We suggest that GABA in extracellular space contributes to reducing variability in motor cortex activity at a resting state and thereby the motor cortex can respond correctly to a subsequent sensory stimulus. Feedback signaling from the motor cortex improves the selective responsiveness of the sensory cortex, which ensures the fidelity of information transmission to the motor cortex, leading to reliable perceptual decision-making.
272.	Reinforcement learning of targeted movement (Chadderdon et al. 2012)
		"Sensorimotor control has traditionally been considered from a control theory perspective, without relation to neurobiology. In contrast, here we utilized a spiking-neuron model of motor cortex and trained it to perform a simple movement task, which consisted of rotating a single-joint “forearm” to a target. Learning was based on a reinforcement mechanism analogous to that of the dopamine system. This provided a global reward or punishment signal in response to decreasing or increasing distance from hand to target, respectively. Output was partially driven by Poisson motor babbling, creating stochastic movements that could then be shaped by learning. The virtual forearm consisted of a single segment rotated around an elbow joint, controlled by flexor and extensor muscles. ..."
273.	Relative spike time coding and STDP-based orientation selectivity in V1 (Masquelier 2012)
		Phenomenological spiking model of the cat early visual system. We show how natural vision can drive spike time correlations on sufficiently fast time scales to lead to the acquisition of orientation-selective V1 neurons through STDP. This is possible without reference times such as stimulus onsets, or saccade landing times. But even when such reference times are available, we demonstrate that the relative spike times encode the images more robustly than the absolute ones.
274.	Respiratory central pattern generator (mammalian brainstem) (Rubin & Smith 2019)
		This model includes a conditional respiratory pacemaker unit (representing the pre-Botzinger Complex), which can be tuned across oscillatory and non-oscillatory dynamic regimes in isolation, embedded into a full respiratory network. The work shows that under this embedding, the pacemaker unit's dynamics become masked: the network exhibits similar dynamical properties regardless of the conditional pacemaker node's tuning, and that node's outputs are dominated by network influences.
275.	Respiratory central pattern generator including Kolliker-Fuse nucleus (Wittman et al 2019)
		We present three highly reduced conductance-based models for the core of the respiratory CPG. All successfully simulate respiratory outputs across eupnoeic and vagotomized conditions and show that loss of inhibition to the pontine Kolliker-Fuse nucleus reproduces the key respiratory alterations associated with Rett syndrome.
276.	Respiratory central pattern generator network in mammalian brainstem (Rubin et al. 2009)
		This model is a reduced version of a spatially organized respiratory central pattern generation network consisting of four neuronal populations (pre-I, early-I, post-I, and aug-E). In this reduction, each population is represented by a single neuron, in an activity-based framework (which includes the persistent sodium current for the pre-I population). The model includes three sources of external drive and can produce several experimentally observed rhythms.
277.	Response of AMPA receptor kinetic model to glutamate release distance (Allam et al., 2015)
		These files model the response of an AMPA receptor kinetic model to the release (and diffusion) of glutamate as a function of distance between receptor and release site.
278.	Response to correlated synaptic input for HH/IF point neuron vs with dendrite (Górski et al 2018)
		" ... Here, we study computational models of neurons to investigate the functional effects of dendritic spikes. In agreement with previous studies, we found that point neurons or neurons with passive dendrites increase their somatic firing rate in response to the correlation of synaptic bombardment for a wide range of input conditions, i.e. input firing rates, synaptic conductances, or refractory periods. However, neurons with active dendrites show the opposite behavior: for a wide range of conditions the firing rate decreases as a function of correlation. We found this property in three types of models of dendritic excitability: a Hodgkin-Huxley model of dendritic spikes, a model with integrate and fire dendrites, and a discrete-state dendritic model. We conclude that fast dendritic spikes confer much broader computational properties to neurons, sometimes opposite to that of point neurons."
279.	Ribbon Synapse (Sikora et al 2005)
		A model of the ribbon synapse was developed to replicate both pre- and postsynaptic functions of this glutamatergic juncture. The presynaptic portion of the model is rich in anatomical and physiological detail and includes multiple release sites for each ribbon based on anatomical studies of presynaptic terminals, presynaptic voltage at the terminal, the activation of voltage-gated calcium channels and a calcium-dependent release mechanism whose rate varies as a function of the calcium concentration that is monitored at two different sites which control both an ultrafast, docked pool of vesicles and a release ready pool of tethered vesicles. See paper for more and details.
280.	Robust transmission in the inhibitory Purkinje Cell to Cerebellar Nuclei pathway (Abbasi et al 2017)
281.	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."
282.	Self-influencing synaptic plasticity (Tamosiunaite et al. 2007)
		"... Similar to a previous study (Saudargiene et al., 2004) we employ a differential Hebbian learning rule to emulate spike-timing dependent plasticity and investigate how the interaction of dendritic and back-propagating spikes, as the post-synaptic signals, could influence plasticity. ..."
283.	Sensorimotor cortex reinforcement learning of 2-joint virtual arm reaching (Neymotin et al. 2013)
		"... We developed a model of sensory and motor neocortex consisting of 704 spiking model-neurons. Sensory and motor populations included excitatory cells and two types of interneurons. Neurons were interconnected with AMPA/NMDA, and GABAA synapses. We trained our model using spike-timing-dependent reinforcement learning to control a 2-joint virtual arm to reach to a fixed target. ... "
284.	Sensory-evoked responses of L5 pyramidal tract neurons (Egger et al 2020)
		This is the L5 pyramidal tract neuron (L5PT) model from Egger, Narayanan et al., Neuron 2020. It allows investigating how synaptic inputs evoked by different sensory stimuli are integrated by the complex intrinsic properties of L5PTs. The model is constrained by anatomical measurements of the subcellular synaptic input patterns to L5PT neurons, in vivo measurements of sensory-evoked responses of different populations of neurons providing these synaptic inputs, and in vitro measurements constraining the biophysical properties of the soma, dendrites and axon (note: the biophysical model is based on the work by Hay et al., Plos Comp Biol 2011). The model files provided here allow performing simulations and analyses presented in Figures 3, 4 and 5.
285.	Shaping NMDA spikes by timed synaptic inhibition on L5PC (Doron et al. 2017)
		This work (published in "Timed synaptic inhibition shapes NMDA spikes, influencing local dendritic processing and global I/O properties of cortical neurons", Doron et al, Cell Reports, 2017), examines the effect of timed inhibition over dendritic NMDA spikes on L5PC (Based on Hay et al., 2011) and CA1 cell (Based on Grunditz et al. 2008 and Golding et al. 2001).
286.	Short term plasticity at the cerebellar granule cell (Nieus et al. 2006)
		The model reproduces short term plasticity of the mossy fibre to granule cell synapse. To reproduce synaptic currents recorded in experiments, a model of presynaptic release was used to determine the concentration of glutamate in the synaptic cleft that ultimately determined a synaptic response. The parameters of facilitation and depression were determined deconvolving AMPA EPSCs.
287.	Short term plasticity of synapses onto V1 layer 2/3 pyramidal neuron (Varela et al 1997)
		This archive contains 3 mod files for NEURON that implement the short term synaptic plasticity model described in Varela, J.A., Sen, K., Gibson, J., Fost, J., Abbott, L.R., and Nelson, S.B.. A quantitative description of short-term plasticity at excitatory synapses in layer 2/3 of rat primary visual cortex. Journal of Neuroscience 17:7926-7940, 1997. Contact ted.carnevale@yale.edu if you have questions about this implementation of the model.
288.	Signal integration in a CA1 pyramidal cell (Graham 2001)
		This model investigates signal integration in the dendritic tree of a hippocampal CA1 pyramidal cell when different combinations of active channels are present in the tree (Graham, 2001)
289.	Simulated cortical color opponent receptive fields self-organize via STDP (Eguchi et al., 2014)
		"... In this work, we address the problem of understanding the cortical processing of color information with a possible mechanism of the development of the patchy distribution of color selectivity via computational modeling. ... Our model of the early visual system consists of multiple topographically-arranged layers of excitatory and inhibitory neurons, with sparse intra-layer connectivity and feed-forward connectivity between layers. Layers are arranged based on anatomy of early visual pathways, and include a retina, lateral geniculate nucleus, and layered neocortex. ... After training with natural images, the neurons display heightened sensitivity to specific colors. ..."
290.	Simulations of modulation of HCN channels in L5PCs (Mäki-Marttunen and Mäki-Marttunen, 2022)
		"... In this work, we build upon existing biophysically detailed models of thick-tufted layer V pyramidal cells and model the effects of over- and under-expression of Ih channels as well as their neuromodulation by dopamine (gain of Ih function) and acetylcholine (loss of Ih function). We show that Ih channels facilitate the action potentials of layer V pyramidal cells in response to proximal dendritic stimulus while they hinder the action potentials in response to distal dendritic stimulus at the apical dendrite. We also show that the inhibitory action of the Ih channels in layer V pyramidal cells is due to the interactions between Ih channels and a hot zone of low voltage-activated Ca2+ channels at the apical dendrite. Our simulations suggest that a combination of Ih-enhancing neuromodulation at the proximal apical dendrite and Ih-inhibiting modulation at the distal apical dendrite can increase the layer V pyramidal excitability more than any of the two neuromodulators alone..."
291.	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. ..."
292.	Single compartment Dorsal Lateral Medium Spiny Neuron w/ NMDA and AMPA (Biddell and Johnson 2013)
		A biophysical single compartment model of the dorsal lateral striatum medium spiny neuron is presented here. The model is an implementation then adaptation of a previously described model (Mahon et al. 2002). The model has been adapted to include NMDA and AMPA receptor models that have been fit to dorsal lateral striatal neurons. The receptor models allow for excitation by other neuron models.
293.	Single compartment: nonlinear a5-GABAAR controls synaptic NMDAR activation (Schulz et al 2018)
		This study shows that IPSCs mediated by a5-subunit containing GABAA receptors are strongly outward-rectifying generating 4-fold larger conductances above -50?mV than at rest. This model shows that synaptic activation of these receptors can very effectively control voltage-dependent NMDA-receptor activation. The files contain the NEURON code for Fig.6 and Fig.7. The model is a single dendritic compartment with one glutamatergic and GABAergic synapse. Physiological properties of GABA synapses were modeled as determined by optogenetic activation of inputs during voltage-clamp recordings in Schulz et al. 2018.
294.	Single E-I oscillating network with amplitude modulation (Avella Gonzalez et al. 2012)
		"... Intriguingly, the amplitude of ongoing oscillations, such as measured in EEG recordings, fluctuates irregularly, with episodes of high amplitude (HAE) alternating with episodes of low amplitude (LAE). ... Here, we show that transitions between HAE and LAE in the alpha/beta frequency band occur in a generic neuronal network model consisting of interconnected inhibitory (I) and excitatory (E) cells that are externally driven by sustained depolarizing currents(cholinergic input) and trains of action potentials that activate excitatory synapses. In the model, action potentials onto inhibitory cells represent input from other brain areas and desynchronize network activity, being crucial for the emergence of amplitude fluctuations. ..."
295.	Single-cell comprehensive biophysical model of SN pars compacta (Muddapu & Chakravarthy 2021)
		Parkinson’s disease (PD) is caused by the loss of dopaminergic cells in substantia nigra pars compacta (SNc), the decisive cause of this inexorable cell loss is not clearly elucidated. We hypothesize that “Energy deficiency at a sub-cellular/cellular/systems-level can be a common underlying cause for SNc cell loss in PD.” Here, we propose a comprehensive computational model of SNc cell which helps us to understand the pathophysiology of neurodegeneration at subcellular-level in PD. We were able to show see how deficits in supply of energy substrates (glucose and oxygen) lead to a deficit in ATP, and furthermore, deficits in ATP are the common factor underlying the pathological molecular-level changes including alpha-synuclein aggregation, ROS formation, calcium elevation, and dopamine dysfunction. The model also suggests that hypoglycemia plays a more crucial role in leading to ATP deficits than hypoxia. We believe that the proposed model provides an integrated modelling framework to understand the neurodegenerative processes underlying PD.
296.	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.
297.	Spatial constrains of GABAergic rheobase shift (Lombardi et al., 2021)
		In this models we investigated how the threshold eGABA, at which GABAergic inhibition switches to excitation, depends on the spatiotemporal constrains in a ball-and-stick neurons and a neurons with a topology derived from an reconstructed neuron.
298.	Spatial summation of excitatory and inhibitory inputs in pyramidal neurons (Hao et al. 2010)
		"... Based on realistic modeling and experiments in rat hippocampal slices, we derived a simple arithmetic rule for spatial summation of concurrent excitatory glutamatergic inputs (E) and inhibitory GABAergic inputs (I). The somatic response can be well approximated as the sum of the excitatory postsynaptic potential (EPSP), the inhibitory postsynaptic potential (IPSP), and a nonlinear term proportional to their product (k*EPSP*IPSP), where the coefficient k reflects the strength of shunting effect. ..."
299.	Spatially-varying glutamate diffusion coefficient at CA1 synaptic cleft space (Gupta et al. 2016)
		Due to the heterogeneous macromolecular crowding and geometrical irregularity at central excitatory synapses, the diffusion coefficient of glutamate may exhibit spatial variation across the cleft space. To take into account the effect of emergent cleft heterogeneity on the generation of excitatory postsynaptic currents (EPSCs), a gamma statistical distribution of the glutamate diffusion coefficient is considered and, using the principle of superstatistics, the glutamate transients are computed as well as the activation of AMPA receptors is performed. This model demonstrates the numerical simulation of the Brownian diffusion of glutamate under distributed diffusion coefficient, the subsequent stochastic activation of AMPA receptors using Milstein-Nicoll scheme and modified Gillespie algorithm with minimum time-step correction, and the eventual stochastic profile of EPSC generation. The study is based on the CA1 synapses located at the dendrites of CA1 pyramidal neurons in the mammalian hippocampal region.
300.	Specific inhibition of dendritic plateau potential in striatal projection neurons (Du et al 2017)
		We explored dendritic plateau potentials in a biophysically detailed SPN model. We coupled the dendritic plateaus to different types of inhibitions (dendritic fast and slow inhibitions, perisomatic inhibition from FS interneurons , etc.) We found the inhibition provides precise control over the plateau potential, and thus the spiking output of SPNs.
301.	Spike burst-pause dynamics of Purkinje cells regulate sensorimotor adaptation (Luque et al 2019)
		"Cerebellar Purkinje cells mediate accurate eye movement coordination. However, it remains unclear how oculomotor adaptation depends on the interplay between the characteristic Purkinje cell response patterns, namely tonic, bursting, and spike pauses. Here, a spiking cerebellar model assesses the role of Purkinje cell firing patterns in vestibular ocular reflex (VOR) adaptation. The model captures the cerebellar microcircuit properties and it incorporates spike-based synaptic plasticity at multiple cerebellar sites. ..."
302.	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. ..."
303.	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
304.	Spine fusion and branching affects synaptic response (Rusakov et al 1996, 1997)
		This compartmental model of a hippocampal granule cell has spinous synapses placed on the second-order dendrites. Changes in shape and connectivity of the spines usually does not effect the synaptic response of the cell unless active conductances are incorporated into the spine membrane (e.g. voltage-dependent Ca2+ channels). With active conductances, spines can generate spike-like events. We showed that changes like fusion and branching, or in fact any increase in the equivalent spine neck resistance, could trigger a dramatic increase in the spine's influence on the dendritic shaft potential.
305.	Spine neck plasticity controls postsynaptic calcium signals (Grunditz et al. 2008)
		This model was set up to dissect the relative contribution of different channels to the spine calcium transients measured at single spines.
306.	State dependent drug binding to sodium channels in the dentate gyrus (Thomas & Petrou 2013)
		A Markov model of sodium channels was developed that includes drug binding to fast inactivated states. This was incorporated into a model of the dentate gyrus to investigate the effects of anti-epileptic drugs on neuron and network properties.
307.	STDP and BDNF in CA1 spines (Solinas et al. 2019)
		Storing memory traces in the brain is essential for learning and memory formation. Memory traces are created by joint electrical activity in neurons that are interconnected by synapses and allow transferring electrical activity from a sending (presynaptic) to a receiving (postsynaptic) neuron. During learning, neurons that are co-active can tune synapses to become more effective. This process is called synaptic plasticity or long-term potentiation (LTP). Timing-dependent LTP (t-LTP) is a physiologically relevant type of synaptic plasticity that results from repeated sequential firing of action potentials (APs) in pre- and postsynaptic neurons. T-LTP is observed during learning in vivo and is a cellular correlate of memory formation. T-LTP can be elicited by different rhythms of synaptic activity that recruit distinct synaptic growth processes underlying t-LTP. The protein brain-derived neurotrophic factor (BDNF) is released at synapses and mediates synaptic growth in response to specific rhythms of t-LTP stimulation, while other rhythms mediate BDNF-independent t-LTP. Here, we developed a realistic computational model that accounts for our previously published experimental results of BDNF-independent 1:1 t-LTP (pairing of 1 presynaptic with 1 postsynaptic AP) and BDNF-dependent 1:4 t-LTP (pairing of 1 presynaptic with 4 postsynaptic APs). The model explains the magnitude and time course of both t-LTP forms and allows predicting t-LTP properties that result from altered BDNF turnover. Since BDNF levels are decreased in demented patients, understanding the function of BDNF in memory processes is of utmost importance to counteract Alzheimer’s disease.
308.	Steady-state Vm distribution of neurons subject to synaptic noise (Rudolph, Destexhe 2005)
		This package simulates synaptic background activity similar to in vivo measurements using a model of fluctuating synaptic conductances, and compares the simulations with analytic estimates. The steady-state membrane potential (Vm) distribution is calculated numerically and compared with the "extended" analytic expression provided in the reference (see this paper for details).
309.	Stochastic calcium mechanisms cause dendritic calcium spike variability (Anwar et al. 2013)
		" ... In single Purkinje cells, spontaneous and synaptically evoked dendritic calcium bursts come in a variety of shapes with a variable number of spikes. The mechanisms causing this variability have never been investigated thoroughly. In this study, a detailed computational model employing novel simulation routines is applied to identify the roles that stochastic ion channels, spatial arrangements of ion channels and stochastic intracellular calcium have towards producing calcium burst variability. … Our findings suggest that stochastic intracellular calcium mechanisms play a crucial role in dendritic calcium spike generation and are, therefore, an essential consideration in studies of neuronal excitability and plasticity."
310.	Stoney vs Histed: Quantifying spatial effects of intracortical microstims (Kumaravelu et al 2022)
		"...We implemented a biophysically-based computational model of a cortical column comprising neurons with realistic morphology and representative synapses. We quantified the spatial effects of single pulses and short trains of ICMS, including the volume of activated neurons and the density of activated neurons as a function of stimulation intensity..."
311.	Striatal D1R medium spiny neuron, including a subcellular DA cascade (Lindroos et al 2018)
		We are investigating how dopaminergic modulation of single channels can be combined to make the D1R possitive MSN more excitable. We also connect multiple channels to substrates of a dopamine induced subcellular cascade to highlight that the classical pathway is too slow to explain DA induced kinetics in the subsecond range (Howe and Dombeck, 2016. doi: 10.1038/nature18942)
312.	Striatal GABAergic microcircuit, dopamine-modulated cell assemblies (Humphries et al. 2009)
		To begin identifying potential dynamically-defined computational elements within the striatum, we constructed a new three-dimensional model of the striatal microcircuit's connectivity, and instantiated this with our dopamine-modulated neuron models of the MSNs and FSIs. A new model of gap junctions between the FSIs was introduced and tuned to experimental data. We introduced a novel multiple spike-train analysis method, and apply this to the outputs of the model to find groups of synchronised neurons at multiple time-scales. We found that, with realistic in vivo background input, small assemblies of synchronised MSNs spontaneously appeared, consistent with experimental observations, and that the number of assemblies and the time-scale of synchronisation was strongly dependent on the simulated concentration of dopamine. We also showed that feed-forward inhibition from the FSIs counter-intuitively increases the firing rate of the MSNs.
313.	Striatal GABAergic microcircuit, spatial scales of dynamics (Humphries et al, 2010)
		The main thrust of this paper was the development of the 3D anatomical network of the striatum's GABAergic microcircuit. We grew dendrite and axon models for the MSNs and FSIs and extracted probabilities for the presence of these neurites as a function of distance from the soma. From these, we found the probabilities of intersection between the neurites of two neurons given their inter-somatic distance, and used these to construct three-dimensional striatal networks. These networks were examined for their predictions for the distributions of the numbers and distances of connections for all the connections in the microcircuit. We then combined the neuron models from a previous model (Humphries et al, 2009; ModelDB ID: 128874) with the new anatomical model. We used this new complete striatal model to examine the impact of the anatomical network on the firing properties of the MSN and FSI populations, and to study the influence of all the inputs to one MSN within the network.
314.	Striatal Spiny Projection Neuron (SPN) plasticity rule (Jedrzejewska-Szmek et al 2016)
315.	Striatal Spiny Projection Neuron, inhibition enhances spatial specificity (Dorman et al 2018)
		We use a computational model of a striatal spiny projection neuron to investigate dendritic spine calcium dynamics in response to spatiotemporal patterns of synaptic inputs. We show that spine calcium elevation is stimulus-specific, with supralinear calcium elevation in cooperatively stimulated spines. Intermediate calcium elevation occurs in neighboring non-stimulated dendritic spines, predicting heterosynaptic effects. Inhibitory synaptic inputs enhance the difference between peak calcium in stimulated spines, and peak calcium in non-stimulated spines, thereby enhancing stimulus specificity.
316.	Striatum D1 Striosome and Matrix Upstates (Prager et al., 2020)
		"...We show that dopamine oppositely shapes responses to convergent excitatory inputs in mouse striosome and matrix striatal spiny projection neurons (SPNs). Activation of postsynaptic D1 dopamine receptors promoted the generation of long-lasting synaptically evoked 'up-states' in matrix SPNs but opposed it in striosomes, which were more excitable under basal conditions. Differences in dopaminergic modulation were mediated, in part, by dendritic voltage-gated calcium channels (VGCCs): pharmacological manipulation of L-type VGCCs reversed compartment-specific responses to D1 receptor activation..."
317.	Structure-dynamics relationships in bursting neuronal networks revealed (Mäki-Marttunen et al. 2013)
		This entry includes tools for generating and analyzing network structure, and for running the neuronal network simulations on them.
318.	Studies of stimulus parameters for seizure disruption using NN simulations (Anderson et al. 2007)
		Architecturally realistic neocortical model using seven classes of excitatory and inhibitory single compartment Hodgkin-Huxley cells. Wiring is adapted to minicolumn hypothesis and incorporates visual and neocortical data. Simulation demonstrates spontaneous bursting onset and cessation, and activity can be altered with external electric field.
319.	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.
320.	Surround Suppression in V1 via Withdraw of Balanced Local Excitation in V1 (Shushruth 2012)
		The model is mean-field network models, which is set up as a so-called ring-model, i. e. it is a highly idealized model of an orientation hypercolumn in primary visual cortex. Long-range intra-areal and inter-areal feedback connections are modeled phenomenologically as an external input. In this model, there are recurrent interactions via short-range local connections between orientation columns, but not between hypercolumns.
321.	Syn Plasticity Regulation + Information Processing in Neuron-Astrocyte Networks (Vuillaume et al 21)
		"... we consider a model of astrocyte-regulated synapses to investigate this hypothesis at the level of layered networks of interacting neurons and astrocytes. Our simulations hint that gliotransmission sustains the transfer function across layers, although it decorrelates the neuronal activity from the signal pattern..."
322.	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.
323.	Synaptic information transfer in computer models of neocortical columns (Neymotin et al. 2010)
		"... We sought to measure how the activity of the network alters information flow from inputs to output patterns. Information handling by the network reflected the degree of internal connectivity. ... With greater connectivity strength, the recurrent network translated activity and information due to contribution of activity from intrinsic network dynamics. ... At still higher internal synaptic strength, the network corrupted the external information, producing a state where little external information came through. The association of increased information retrieved from the network with increased gamma power supports the notion of gamma oscillations playing a role in information processing."
324.	Synaptic integration by MEC neurons (Justus et al. 2017)
		Pyramidal cells, stellate cells and fast-spiking interneurons receive running speed dependent glutamatergic input from septo-entorhinal projections. These models simulate the integration of this input by the different MEC celltypes.
325.	Synaptic integration in a model of granule cells (Gabbiani et al 1994)
		We have developed a compartmental model of a turtle cerebellar granule cell consisting of 13 compartments that represent the soma and 4 dendrites. We used this model to investigate the synaptic integration of mossy fiber inputs in granule cells. See reference or abstract at PubMed link below for more information.
326.	Synaptic integration in tuft dendrites of layer 5 pyramidal neurons (Larkum et al. 2009)
		Simulations used in the paper. Voltage responses to current injections in different tuft locations; NMDA and calcium spike generation. Summation of multiple input distribution.
327.	Synaptic plasticity: pyramid->pyr and pyr->interneuron (Tsodyks et al 1998)
		An implementation of a model of short-term synaptic plasticity with NEURON. The model was originally described by Tsodyks et al., who assumed that the synapse acted as a current source, but this implementation treats it as a conductance change. Tsodyks, M., Pawelzik, K., Markram, H. Neural networks with dynamic synapses. Neural Computation 10:821-835, 1998. Tsodyks, M., Uziel, A., Markram, H. Synchrony generation in recurrent networks with frequency-dependent synapses. J. Neurosci. 2000 RC50.
328.	Synaptic scaling balances learning in a spiking model of neocortex (Rowan & Neymotin 2013)
		Learning in the brain requires complementary mechanisms: potentiation and activity-dependent homeostatic scaling. We introduce synaptic scaling to a biologically-realistic spiking model of neocortex which can learn changes in oscillatory rhythms using STDP, and show that scaling is necessary to balance both positive and negative changes in input from potentiation and atrophy. We discuss some of the issues that arise when considering synaptic scaling in such a model, and show that scaling regulates activity whilst allowing learning to remain unaltered.
329.	Synaptic vesicle fusion model (Church et al 2021)
		These parameter files define Cell simulations of glutamate release and receptor binding at synapses. Four basic models are included that vary, the pore diameter of a fusing vesicle from full fusion (FullFusion) to a variable sized pore from a small as 0.4nm (DelayFusion), that vary the umber of fusing vesicles (Multivesicular) or that vary the position of the fusing vesicle with the post synaptic glutamate receptors (Clustered receptors). Our work demonstrates that experimental effects on release and low affinity antagonism are well-fit by reduced release rates of glutamate from a restricted pore.
330.	Synchrony by synapse location (McTavish et al. 2012)
		This model considers synchrony between mitral cells induced via shared granule cell interneurons while taking into account the spatial constraints of the system. In particular, since inhibitory inputs decay passively along the lateral dendrites, this model demonstrates that an optimal arrangement of the inhibitory synapses will be near the cell bodies of the relevant mitral cells.
331.	Synthesis of spatial tuning functions from theta cell spike trains (Welday et al., 2011)
		A single compartment model reproduces the firing rate maps of place, grid, and boundary cells by receiving inhibitory inputs from theta cells. The theta cell spike trains are modulated by the rat's movement velocity in such a way that phase interference among their burst pattern creates spatial envelope function which simulate the firing rate maps.
332.	Temporal integration by stochastic recurrent network (Okamoto et al. 2007)
		"Temporal integration of externally or internally driven information is required for a variety of cognitive processes. This computation is generally linked with graded rate changes in cortical neurons, which typically appear during a delay period of cognitive task in the prefrontal and other cortical areas. Here, we present a neural network model to produce graded (climbing or descending) neuronal activity. Model neurons are interconnected randomly by AMPA-receptor–mediated fast excitatory synapses and are subject to noisy background excitatory and inhibitory synaptic inputs. In each neuron, a prolonged afterdepolarizing potential follows every spike generation. Then, driven by an external input, the individual neurons display bimodal rate changes between a baseline state and an elevated firing state, with the latter being sustained by regenerated afterdepolarizing potentials. ..."
333.	Thalamic network model of deep brain stimulation in essential tremor (Birdno et al. 2012)
		"... Thus the decreased effectiveness of temporally irregular DBS trains is due to long pauses in the stimulus trains, not the degree of temporal irregularity alone. We also conducted computer simulations of neuronal responses to the experimental stimulus trains using a biophysical model of the thalamic network. Trains that suppressed tremor in volunteers also suppressed fluctuations in thalamic transmembrane potential at the frequency associated with cerebellar burst-driver inputs. Clinical and computational findings indicate that DBS suppresses tremor by masking burst-driver inputs to the thalamus and that pauses in stimulation prevent such masking. Although stimulation of other anatomic targets may provide tremor suppression, we propose that the most relevant neuronal targets for effective tremor suppression are the afferent cerebellar fibers that terminate in the thalamus."
334.	Thalamic neuron, zebra finch DLM: Integration of pallidal and cortical inputs (Goldberg et al. 2012)
		This is a single-compartment model of a zebra finch thalamic relay neuron from nucleus DLM. It is used to explore the interaction between cortex-like glutamatergic input and pallidum-like GABAergic input as they control the spiking output of these neurons.
335.	Thalamic transformation of pallidal input (Hadipour-Niktarash 2006)
		"In Parkinson’s disease, neurons of the internal segment of the globus pallidus (GPi) display the low-frequency tremor-related oscillations. These oscillatory activities are transmitted to the thalamic relay nuclei. Computer models of the interacting thalamocortical (TC) and thalamic reticular (RE) neurons were used to explore how the TC-RE network processes the low-frequency oscillations of the GPi neurons. ..."
336.	Thalamocortical loop with delay for investigation of absence epilepsy (Liu et al 2019)
		Conductance based network model of one thalamic reticular neuron, one thalamic pyramidal neuron and one cortical pyramidal neuron. Used to show that large delay in the corticothalamic connection can lead to multistability.
337.	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)
338.	Thalamocortical augmenting response (Bazhenov et al 1998)
		In the cortical model, augmenting responses were more powerful in the "input" layer compared with those in the "output" layer. Cortical stimulation of the network model produced augmenting responses in cortical neurons in distant cortical areas through corticothalamocortical loops and low-threshold intrathalamic augmentation. ... The predictions of the model were compared with in vivo recordings from neurons in cortical area 4 and thalamic ventrolateral nucleus of anesthetized cats. The known intrinsic properties of thalamic cells and thalamocortical interconnections can account for the basic properties of cortical augmenting responses. See reference for details. NEURON implementation note: cortical SU cells are getting slightly too little stimulation - reason unknown.
339.	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. ..."
340.	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.
341.	The electrodiffusive neuron-extracellular-glia (edNEG) model (Sætra et al. 2021)
		"... We here present the electrodiffusive neuron-extracellular-glia (edNEG) model, which we believe is the first model to combine compartmental neuron modeling with an electrodiffusive framework for intra- and extracellular ion concentration dynamics in a local piece of neuro-glial brain tissue. The edNEG model (i) keeps track of all intraneuronal, intraglial, and extracellular ion concentrations and electrical potentials, (ii) accounts for action potentials and dendritic calcium spikes in neurons, (iii) contains a neuronal and glial homeostatic machinery that gives physiologically realistic ion concentration dynamics, (iv) accounts for electrodiffusive transmembrane, intracellular, and extracellular ionic movements, and (v) accounts for glial and neuronal swelling caused by osmotic transmembrane pressure gradients. The edNEG model accounts for the concentration-dependent effects on ECS potentials that the standard models neglect. Using the edNEG model, we analyze these effects by splitting the extracellular potential into three components: one due to neural sink/source configurations, one due to glial sink/source configurations, and one due to extracellular diffusive currents ..."
342.	The origin of different spike and wave-like events (Hall et al 2017)
		Acute In vitro models have revealed a great deal of information about mechanisms underlying many types of epileptiform activity. However, few examples exist that shed light on spike and wave (SpW) patterns of pathological activity. SpW are seen in many epilepsy syndromes, both generalised and focal, and manifest across the entire age spectrum. They are heterogeneous in terms of their severity, symptom burden and apparent anatomical origin (thalamic, neocortical or both), but any relationship between this heterogeneity and underlying pathology remains elusive. Here we demonstrate that physiological delta frequency rhythms act as an effective substrate to permit modelling of SpW of cortical origin and may help to address this issue. ..."
343.	The role of glutamate in neuronal ion homeostasis: spreading depolarization (Hubel et al 2017)
		This model includes ion concentration dynamics (sodium, potassium, chloride) inside and outside the neuron, the exchange of ions with glia and blood vessels, volume dynamics of neuron, glia, and extracellular space, glutamate homeostasis involving release by neuron and uptake by both neuron and glia. Spreading depolarization is used as a case study.
344.	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."
345.	Turtle visual cortex model (Nenadic et al. 2003, Wang et al. 2005, Wang et al. 2006)
		This is a model of the visual cortex of freshwater turtles that is based upon the known anatomy and physiology of individual neurons. The model was published in three papers (Nenadic et al., 2003; Wang et al., 2005; Wang et al., 2006), which should be consulted for full details on its construction. The model has also been used in several papers (Robbins and Senseman, 2004; Du et al., 2005; Du et al., 2006). It is implemented in GENESIS (Bower and Beeman, 1998).
346.	Two populations of excitatory neurons in the superficial retrosplenial cortex (Brennan et al 2020)
		Hyperexcitable neurons enable precise and persistent information encoding in the superficial retrosplenial cortex
347.	Two-neuron conductance-based model with dynamic ion concentrations to study NaV1.1 channel mutations
		Gain of function mutations of SCN1A, the gene coding for the voltage-gated sodium channel NaV1.1, cause familial hemiplegic migraine type 3 (FHM-3), whereas loss of function mutations cause different types of epilepsy. To study those mutations, we developed a two-neuron conductance-based model of interconnected GABAergic and pyramidal glutamatergic neurons, with dynamic ion concentrations. We modeled FHM-3 mutations with persistent sodium current in the GABAergic neuron and epileptogenic mutations by decreasing the fast-inactivating sodium conductance in the GABAergic neuron.
348.	Unbalanced peptidergic inhibition in superficial cortex underlies seizure activity (Hall et al 2015)
		" ...Loss of tonic neuromodulatory excitation, mediated by nicotinic acetylcholine or serotonin (5HT3A) receptors, of 5HT3-immunopositive interneurons caused an increase in amplitude and slowing of the delta rhythm until each period became the "wave" component of the spike and wave discharge. As with the normal delta rhythm, the wave of a spike and wave discharge originated in cortical layer 5. In contrast, the "spike" component of the spike and wave discharge originated from a relative failure of fast inhibition in layers 2/3-switching pyramidal cell action potential outputs from single, sparse spiking during delta rhythms to brief, intense burst spiking, phase-locked to the field spike. The mechanisms underlying this loss of superficial layer fast inhibition, and a concomitant increase in slow inhibition, appeared to be precipitated by a loss of neuropeptide Y (NPY)-mediated local circuit inhibition and a subsequent increase in vasoactive intestinal peptide (VIP)-mediated disinhibition. Blockade of NPY Y1 receptors was sufficient to generate spike and wave discharges, whereas blockade of VIP receptors almost completely abolished this form of epileptiform activity. These data suggest that aberrant, activity-dependent neuropeptide corelease can have catastrophic effects on neocortical dynamics."
349.	Using Strahler's analysis to reduce realistic models (Marasco et al, 2013)
		Building on our previous work (Marasco et al., (2012)), we present a general reduction method based on Strahler's analysis of neuron morphologies. We show that, without any fitting or tuning procedures, it is possible to map any morphologically and biophysically accurate neuron model into an equivalent reduced version. Using this method for Purkinje cells, we demonstrate how run times can be reduced up to 200-fold, while accurately taking into account the effects of arbitrarily located and activated synaptic inputs.
350.	Visual Cortex Neurons: Dendritic computations (Archie, Mel 2000)
		Neuron and C program files from Archie, K.A. and Mel, B.W. A model of intradendritic computation of binocular disparity. Nature Neuroscience 3:54-63, 2000 The original files for this model are located at the web site http://www-lnc.usc.edu/~karchie/synmap
351.	Visual Cortex Neurons: Dendritic study (Anderson et al 1999)
		Neuron mod and hoc files for the paper: Anderson, J.C. Binzegger, T., Kahana, O., Segev, I., and Martin, K.A.C Dendritic asymmetry cannot account for directional responses in visual cortex. Nature Neuroscience 2:820:824, 1999
352.	Voltage attenuation in CA1 pyramidal neuron dendrites (Golding et al 2005)
		Voltage attenuation in the apical dendritic field of CA1 pyramidal neurons is particularly strong for epsps spreading toward the soma. High cytoplasmic resistivity and high membrane (leak) conductance appear to be the major determinants of voltage attenuation over most of the apical field, but H current may be responsible for as much as half of the attenuation of distal apical epsps.
353.	VTA dopamine neuron (Tarfa, Evans, and Khaliq 2017)
		In our model of a midbrain VTA dopamine neuron, we show that the decay kinetics of the A-type potassium current can control the timing of rebound action potentials.

Re-display model names without descriptions