ModelDB: Model Database User: Public
 SenseLab 
Skip Navigation Links
Computational model List
Page size: Page of 10
Name/Notes
1
3D model of the olfactory bulb (Migliore et al. 2014) 
 This entry contains a link to a full HD version of movie 1 and the NEURON code of the paper: "Distributed organization of a brain microcircuit analysed by three-dimensional modeling: the olfactory bulb" by M Migliore, F Cavarretta, ML Hines, and GM Shepherd.
2
A 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.
3
A cardiac cell simulator (Puglisi and Bers 2001), applied to the QT interval (Busjahn et al 2004) 
 "LabHEART is an easy to use program that simulates the cardiac action potential, calcium transient and ionic currents. Key parameters such as ionic concentration, stimulus waveform and channel conductance can easily be changed by a click on an icon or dragging a slider. It is a powerfull tool for teaching and researching cardiac electrophysiology."
4
A comparative computer simulation of dendritic morphology (Donohue and Ascoli 2008) 
 Morphological aspects of dendritic branching such branch lengths, taper rates,ratios of daughter radii, and bifurcation probabilities are measured from real cells. These morphometrics are then resampled to create virtual trees based on the current branch order, radius, path distance to the soma, or combination of the three.
5
A contracting model of the basal ganglia (Girard et al. 2008) 
 Basal ganglia model : selection processes between channels, dynamics controlled by contraction analysis, rate-coding model of neurons based on locally projected dynamical systems (lPDS).
6
A CORF computational model of a simple cell that relies on LGN input (Azzopardi & Petkov 2012) 
 "... We propose a computational model that uses as afferent inputs the responses of model LGN cells with center-surround receptive fields (RFs) and we refer to it as a Combination of Receptive Fields (CORF) model. We use shifted gratings as test stimuli and simulated reverse correlation to explore the nature of the proposed model. We study its behavior regarding the effect of contrast on its response and orientation bandwidth as well as the effect of an orthogonal mask on the response to an optimally oriented stimulus. We also evaluate and compare the performances of the CORF and GF (Gabor Filter) models regarding contour detection, using two public data sets of images of natural scenes with associated contour ground truths. ... The proposed CORF model is more realistic than the GF model and is more effective in contour detection, which is assumed to be the primary biological role of simple cells."
7
A cortical sheet mesoscopic model for investigating focal seizure onset dynamics (Wang et al. 2014) 
 The model uses realistically coupled, discretised, Wilson-Cowan units to describe the spatio-temporal activity of a cortical sheet. This model has been used the investigate the dynamic onset mechanisms of focal seizures.
8
A detailed and fast model of extracellular recordings (Camunas-Mesa & Qurioga 2013) 
 "We present a novel method to generate realistic simulations of extracellular recordings. The simulations were obtained by superimposing the activity of neurons placed randomly in a cube of brain tissue. Detailed models of individual neurons were used to reproduce the extracellular action potentials of close-by neurons. ..."
9
A dual-Ca2+-sensor model for neurotransmitter release in a central synapse (Sun et al. 2007) 
 "Ca2+-triggered synchronous neurotransmitter release is well described, but asynchronous release-in fact, its very existence-remains enigmatic. Here we report a quantitative description of asynchronous neurotransmitter release in calyx-of-Held synapses. ... Our results reveal that release triggered in wild-type synapses at low Ca2+ concentrations is physiologically asynchronous, and that asynchronous release completely empties the readily releasable pool of vesicles during sustained elevations of Ca2+. We propose a dual-Ca2+-sensor model of release that quantitatively describes the contributions of synchronous and asynchronous release under conditions of different presynaptic Ca2+ dynamics."
10
A dynamic model of the canine ventricular myocyte (Hund, Rudy 2004) 
 The Hund-Rudy dynamic (HRd) model is based on data from the canine epicardial ventricular myocyte. Rate-dependent phenomena associated with ion channel kinetics, action potential properties and Ca2+ handling are simulated by the model. See paper for more and details.
11
A dynamical model of the basal ganglia (Leblois et al 2006) 
 We propose a new model for the function and dysfunction of the basal ganglia (BG). The basal ganglia are a set of cerebral structures involved in motor control which dysfunction causes high-incidence pathologies such as Parkinson's disease (PD). Their precise motor functions remain unknown. The classical model of the BG that allowed for the discovery of new treatments for PD seems today outdated in several respects. Based on experimental observations, our model proposes a simple dynamical framework for the understanding of how BG may select motor programs to be executed. Moreover, we explain how this ability is lost and how tremor-related oscillations in neuronal activity may emerge in PD.
12
A fast model of voltage-dependent NMDA Receptors (Moradi et al. 2013) 
 These are two or triple-exponential models of the voltage-dependent NMDA receptors. Conductance of these receptors increase voltage-dependently with a "Hodgkin and Huxley-type" gating style that is also depending on glutamate-binding. Time course of the gating of these receptors in response to glutamate are also changing voltage-dependently. Temperature sensitivity and desensitization of these receptor are also taken into account. Three previous kinetic models that are able to simulate the voltage-dependence of the NMDARs are also imported to the NMODL. These models are not temperature sensitive. These models are compatible with the "event delivery system" of NEURON. Parameters that are reported in our paper are applicable to CA1 pyramidal cell dendrites.
13
A Fast Rhythmic Bursting Cell: in vivo cell modeling (Lee 2007) 
 One of the cellular mechanisms underlying the generation of gamma oscillations is a type of cortical pyramidal neuron named fast rhythmic bursting (FRB) cells. After cells from cats' primary visual cortices were filled with Neurobiotin, the brains were cut, and the cells were photographed. One FRB cell was chosen to be confocaled, reconstructed with Neurolucida software, and generated a detailed multi-compartmental model in the NEURON program. We explore firing properties of FRB cells and the role of enhanced Na+ conductance.
14
A finite volume method for stochastic integrate-and-fire models (Marpeau et al. 2009) 
 "The stochastic integrate and fire neuron is one of the most commonly used stochastic models in neuroscience. Although some cases are analytically tractable, a full analysis typically calls for numerical simulations. We present a fast and accurate finite volume method to approximate the solution of the associated Fokker-Planck equation. ..."
15
A four compartmental model for ABPD complex in crustacean pyloric network (Maran et al. 2011) 
 "Central pattern generators (CPGs) frequently include bursting neurons that serve as pacemakers for rhythm generation. Phase resetting curves (PRCs) can provide insight into mechanisms underlying phase locking in such circuits. PRCs were constructed for a pacemaker bursting complex in the pyloric circuit in the stomatogastric ganglion of the lobster and crab. ..."
16
A generic MAPK cascade model for random parameter sampling analysis (Mai and Liu 2013) 
 A generic three-tier MAPK cascade model constructed by comparing previous MAPK models covering a range of biosystems. Pseudo parameters and random sampling were employed for qualitative analysis. A range of kinetic behaviors of MAPK activation, including ultrasensitivity, bistability, transient activation and oscillation, were successfully reproduced in this generic model. The mechanisms were revealed by statistic analysis of the parameter sets.
17
A kinetic model of dopamine- and calcium-dependent striatal synaptic plasticity (Nakano et al. 2010) 
 A signaling pathway model of spines that express D1-type dopamine receptors was constructed to analyze the dynamic mechanisms of dopamine- and calcium-dependent plasticity. The model incorporated all major signaling molecules, including dopamine- and cyclic AMP-regulated phosphoprotein with a molecular weight of 32 kDa (DARPP32), as well as AMPA receptor trafficking in the post-synaptic membrane. Simulations with dopamine and calcium inputs reproduced dopamine- and calcium-dependent plasticity. Further in silico experiments revealed that the positive feedback loop consisted of protein kinase A (PKA), protein phosphatase 2A (PP2A), and the phosphorylation site at threonine 75 of DARPP-32 (Thr75) served as the major switch for inducing LTD and LTP. The present model elucidated the mechanisms involved in bidirectional regulation of corticostriatal synapses and will allow for further exploration into causes and therapies for dysfunctions such as drug addiction."
18
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. ..."
19
A large-scale model of the functioning brain (spaun) (Eliasmith et al. 2012) 
 " ... In this work, we present a 2.5-million-neuron model of the brain (called “Spaun”) that bridges this gap (between neural activity and biological function) by exhibiting many different behaviors. The model is presented only with visual image sequences, and it draws all of its responses with a physically modeled arm. Although simplified, the model captures many aspects of neuroanatomy, neurophysiology, and psychological behavior, which we demonstrate via eight diverse tasks."
20
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.
21
A method of counting motor units in mice (Major et al 2007) 
 "... Our goal was to develop an efficient method to determine the number of motor neurons making functional connections to muscle in a transgenic mouse model of amyotrophic lateral sclerosis (ALS). We developed a novel protocol for motor unit number estimation (MUNE) using incremental stimulation. The method involves analysis of twitch waveforms using a new software program, ITS-MUNE, designed for interactive calculation of motor unit number. The method was validated by testing simulated twitch data from a mathematical model of the neuromuscular system. Computer simulations followed the same stimulus-response protocol and produced waveform data that were indistinguishable from experiments. ... The ITS-MUNE analysis method has the potential to quantitatively measure the progression of motor neuron diseases and therefore the efficacy of treatments designed to alleviate pathologic processes of muscle denervation." The software is available for download under the "ITS-MUNE software" link at http://www.uofaweb.ualberta.ca/compneurolab/MUNE.cfm
22
A microcircuit model of the frontal eye fields (Heinzle et al. 2007) 
 " ... we show that the canonical circuit (Douglas et al. 1989, Douglas and Martin 1991) can, with a few modifications, model the primate FEF. The spike-based network of integrate-and-fire neurons was tested in tasks that were used in electrophysiological experiments in behaving macaque monkeys. The dynamics of the model matched those of neurons observed in the FEF, and the behavioral results matched those observed in psychophysical experiments. The close relationship between the model and the cortical architecture allows a detailed comparison of the simulation results with physiological data and predicts details of the anatomical circuit of the FEF."
23
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. …”
24
A model for how correlation depends on the neuronal excitability type (Hong et al. 2012) 
 “ … Using simulations and experiments in rat hippocampal neurons, we show here that pairs of neurons receiving correlated input also exhibit correlations arising from precise spike-time synchronization. Contrary to rate comodulation, spike-time synchronization is unaffected by firing rate, thus enabling synchrony- and rate-based coding to operate independently. The type of output correlation depends on whether intrinsic neuron properties promote integration or coincidence detection: “ideal” integrators (with spike generation sensitive to stimulus mean) exhibit rate comodulation, whereas ideal coincidence detectors (with spike generation sensitive to stimulus variance) exhibit precise spike-time synchronization. … Our results explain how different types of correlations arise based on how individual neurons generate spikes, and why spike-time synchronization and rate comodulation can encode different stimulus properties. Our results also highlight the importance of neuronal properties for population-level coding insofar as neural networks can employ different coding schemes depending on the dominant operating mode of their constituent neurons. “
25
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.
26
A model for pituitary GH(3) lactotroph (Wu and Chang 2005) 
 The ATP-sensitive K(+) (K(ATP)) channels are composed of sulfonylurea receptor and inwardly rectifying K(+) channel (Kir6.2) subunit. These channels are regulated by intracellular ADP/ATP ratio and play a role in cellular metabolism. ... The objective of this study was to determine whether Diethyl pyrocarbonate (DEPC) modifies K(ATP)-channel activity in pituitary GH(3) cells. ... Simulation studies also demonstrated that the increased conductance of K(ATP)-channels used to mimic DEPC actions reduced the frequency of spontaneous action potentials and fluctuation of intracellular Ca(2+). The results indicate that chemical modification with DEPC enhances K(ATP)-channel activity and influences functional activities of pituitary GH(3) cells. See paper for more and details.
27
A model of beta-adrenergic modulation of IKs in the guinea-pig ventricle (Severi et al. 2009) 
 Detailed understanding of IKs gating complexity may provide clues on the mechanisms of cardiac repolarization instability and the resulting arrhythmias. We developed and tested a kinetic Markov model to interpret physiologically relevant IKs properties, including pause-dependency and modulation by beta-adrenergic receptors (beta-AR). The model was developed from the Silva & Rudy formulation. Parameters were optimized on control and ISO experimental data, respectively.
28
A model of local field potentials generated by medial superior olive neurons (Goldwyn et al 2014) 
 A computational model of local field potentials generated by medial superior olive neurons. These field potentials are known as the "auditory neurophonic". MSO neuron is modeled as a soma and two dendrites (following Mathews et al, Nature Neurosci, 2010). Intracellular and a 1D extracellular domain are dynamically coupled and solved to simulate spatial-temporal patterns of membrane voltage and extracellular voltage in response to trains of synaptic inputs (monolateral or bilateral, excitation and/or inhibition). The model produces spatio-temporal patterns similar to neurophonic responses recorded in vivo, as discussed in the accompanying manuscript.
29
A Model of Multiple Spike Initiation Zones in the Leech C-interneuron (Crisp 2009) 
 The leech C-interneuron and its electrical synapse with the S-interneuron exhibit unusual properties: an asymmetric delay when impulses travel from one soma to the other, and graded C-interneuron impulse amplitudes under elevated divalent cation concentrations. These properties have been simulated using a SNNAP model in which the C-interneuron has multiple, independent spike initiation zones associated with individual electrical junctions with the C-interneuron.
30
A Model of Selection between Stimulus and Place Strategy in a Hawkmoth (Balkenius et al. 2004) 
 "In behavioral experiments, the hawkmoth Deilephila elpenor can learn both the color and the position of artificial flowers. ... We show how a computational model can reproduce the behavior in the experimental situation. The aim of the model is to investigate which learning and behavior selection strategies are necessary to reproduce the behavior observed in the experiment. The model is based on behavioral data and the sensitivities of the moth photoreceptors. The model consists of a number of interacting behavior systems that are triggered by specific stimuli and control specific behaviors. The ability of the moth to learn the colors of different flowers and the adaptive processes involved in the choice between stimulus-approach and place-approach strategies are reproduced very accurately by the model. The model has implications both for further studies of the ecology of the animal and for robotic systems."
31
A model of the femur-tibia control system in stick insects (Stein et al. 2008) 
 We studied the femur-tibia joint control system of the insect leg, and its switch between resistance reflex in posture control and "active reaction" in walking. The "active reaction" is basically a reversal of the resistance reflex. Both responses are elicited by the same sensory input and the same neuronal network (the femur-tibia network). The femur-tibia network was modeled by fitting the responses of model neurons to those obtained in animals. Each implemented neuron has a physiological counterpart. The strengths of 16 interneuronal pathways that integrate sensory input were then assigned three different values and varied independently, generating a database of more than 43 million network variants. The uploaded version contains the model that best represented the resistance reflex. Please see the README for more help. We demonstrate that the combinatorial code of interneuronal pathways determines motor output. A switch between different behaviors such as standing to walking can thus be achieved by altering the strengths of selected sensory integration pathways.
32
A model of the temporal pattern generator of C. elegans egg-laying behavior (Zhang et. al 2010) 
 "... We suggest that the HSN neuron is the executive neuron driving egg-laying events. We propose that the VC neurons act as "single egg counters" that inhibit HSN activity for short periods in response to individual egg-laying events. We further propose that the uv1 neuroendocrine cells are "cluster counters", which inhibit HSN activity for longer periods and are responsible for the time constant of the inactive phase. Together they form an integrated circuit that drives the clustered egg-laying pattern. ..."
33
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.
34
A Moth MGC Model-A HH network with quantitative rate reduction (Buckley & Nowotny 2011) 
 We provide the model used in Buckley & Nowotny (2011). It consists of a network of Hodgkin Huxley neurons coupled by slow GABA_B synapses which is run alongside a quantitative reduction described in the associated paper.
35
A multi-compartment model for interneurons in the dLGN (Halnes et al. 2011) 
 This model for dLGN interneurons is presented in two parameterizations (P1 & P2), which were fitted to current-clamp data from two different interneurons (IN1 & IN2). The model qualitatively reproduces the responses in IN1 & IN2 under 8 different experimental condition, and quantitatively reproduces the I/O-relations (#spikes elicited as a function of injected current).
36
A multiphysics neuron model for cellular volume dynamics (Lee et al. 2011) 
 This paper introduces a novel neuron model, where the cell volume is a time-varying variable and multiple physical principles are combined to build governing equations. Using this model, we analyzed neuronal volume responses during excitation, which elucidated the waveforms of fast intrinsic optical signals observed experimentally across the literature. In addition, we analyzed volume responses on a longer time scale with repetitive stimulation to study the characteristics of slow cell swelling.
37
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.
38
A network model of the vertebrate retina (Publio et al. 2009) 
 In this work, we use a minimal conductance-based model of the ON rod pathways in the vertebrate retina to study the effects of electrical synaptic coupling via gap junctions among rods and among AII amacrine cells on the dynamic range of the retina. The model is also used to study the effects of the maximum conductance of rod hyperpolarization activated current Ih on the dynamic range of the retina, allowing a study of the interrelations between this intrinsic membrane parameter with those two retina connectivity characteristics.
39
A Neural mass computational model of the Thalamocorticothalamic circuitry (Bhattacharya et al. 2011) 
 The model presented here is a bio-physically plausible version of a simple thalamo-cortical neural mass computational model proposed by Lopes da Silva in 1974 to simulate brain EEG activity within the alpha band (8-13 Hz). The thalamic and cortical circuitry are presented as separate modules in this model with cell populations as in biology. The connectivity between cell populations are as reported by Sherman, S. in Scholarpedia, 2006. The values of the synaptic connectivity parameters are as reported by Van Horn et al, 2000. In our paper (doi:10.1016/j.neunet.2011.02.009), we study the model behaviour while varying the values of the synaptic connectivity parameters (Cyyy) in the model about their respective 'basal' (intial) values.
40
A neural model of Parkinson`s disease (Cutsuridis and Perantonis 2006, Cutsuridis 2006, 2007) 
 "A neural model of neuromodulatory (dopamine) control of arm movements in Parkinson’s disease (PD) bradykinesia was recently introduced [1, 2]. The model is multi-modular consisting of a basal ganglia module capable of selecting the most appropriate motor command in a given context, a cortical module for coordinating and executing the final motor commands, and a spino-musculo-skeletal module for guiding the arm to its final target and providing proprioceptive (feedback) input of the current state of the muscle and arm to higher cortical and lower spinal centers. ... The new (extended) model [3] predicted that the reduced reciprocal disynaptic Ia inhibition in the DA depleted case doesn’t lead to the co-contraction of antagonist motor units." See below readme and papers for more and details.
41
A neurocomputational model of classical conditioning phenomena (Moustafa et al. 2009) 
 "... Here, we show that the same information-processing function proposed for the hippocampal region in the Gluck and Myers (1993) model can also be implemented in a network without using the backpropagation algorithm. Instead, our newer instantiation of the theory uses only (a) Hebbian learning methods which match more closely with synaptic and associative learning mechanisms ascribed to the hippocampal region and (b) a more plausible representation of input stimuli. We demonstrate here that this new more biologically plausible model is able to simulate various behavioral effects, including latent inhibition, acquired equivalence, sensory preconditioning, negative patterning, and context shift effects. ..."
42
A nicotinic acetylcholine receptor kinetic model (Edelstein et al. 1996) 
 Nicotinic acetylcholine receptors are transmembrane oligomeric proteins that mediate interconversions between open and closed channel states under the control of neurotransmitters. .. In order to represent the functional properties of such receptors, we have developed a kinetic model that links conformational interconversion rates to agonist binding and extends the general principles of the Monod- Wyman-Changeux model of allosteric transitions. ... Application of the model to the peripheral nicotinic acetylcholine receptor (nAChR) accounts for the main properties of ligand-gating, including single-channel events, and several new relationships are predicted. ... In terms of future developments, the analysis presented here provides a physical basis for constructing more biologically realistic models of synaptic modulation that may be applied to artificial neural networks.
43
A reinforcement learning example (Sutton and Barto 1998) 
 This MATLAB script demonstrates an example of reinforcement learning functions guiding the movements of an agent (a black square) in a gridworld environment. See at the top of the matlab script and the book for more details.
44
A set of reduced models of layer 5 pyramidal neurons (Bahl et al. 2012) 
 These are the NEURON files for 10 different models of a reduced L5 pyramidal neuron. The parameters were obtained by automatically fitting the models to experimental data using a multi objective evolutionary search strategy. Details on the algorithm can be found at www.g-node.org/emoo and in Bahl et al. (2012).
45
A simple integrative electrophysiological model of bursting GnRH neurons (Csercsik et al. 2011) 
 In this paper a modular model of the GnRH neuron is presented. For the aim of simplicity, the currents corresponding to fast time scales and action potential generation are described by an impulsive system, while the slower currents and calcium dynamics are described by usual ordinary differential equations (ODEs). The model is able to reproduce the depolarizing afterpotentials, afterhyperpolarization, periodic bursting behavior and the corresponding calcium transients observed in the case of GnRH neurons.
46
A simplified cerebellar Purkinje neuron (the PPR model) (Brown et al. 2011) 
 These models were implemented in NEURON by Sherry-Ann Brown in the laboratory of Leslie M. Loew. The files reproduce Figures 2c-f from Brown et al, 2011 "Virtual NEURON: a Strategy For Merged Biochemical and Electrophysiological Modeling".
47
A simplified model of NMDA oscillations in lamprey locomotor neurons (Huss et al. 2008) 
 Using experiments in conjunction with this simplified model, we sought to understand the basic mechanisms behind NMDA-induced oscillations in lamprey locomotor neurons, specifically (a) how the oscillation frequency depends on NMDA concentration and why, and (b) what the minimal number of components for generating NMDA oscillations is (in vitro and in the model).
48
A simulation method for the firing sequences of motor units (Jiang et al 2006) 
 " ... a novel model based on the Hodgkin–Huxley (HH) system is proposed, which has the ability to simulate the complex neurodynamics of the firing sequences of motor neurons. The model is presented at the cellular level and network level, and some simulation results from a simple 3-neuron network are presented to demonstrate its applications." See paper for more and details.
49
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.
50
A spatially extended model for macroscopic spike-wave discharges (Taylor and Baier 2011) 
 A spatially extended neural field model for generating spike-wave based on the Amari (1977) model implemented in MATLAB.
51
A spiking model of cortical broadcast and competition (Shanahan 2008) 
 "This paper presents a computer model of cortical broadcast and competition based on spiking neurons and inspired by the hypothesis of a global neuronal workspace underlying conscious information processing in the human brain. In the model, the hypothesised workspace is realised by a collection of recurrently interconnected regions capable of sustaining and disseminating a reverberating spatial pattern of activation. ..."
52
A theory of ongoing activity in V1 (Goldberg et al 2004) 
 Ongoing spontaneous activity in the cerebral cortex exhibits complex spatiotemporal patterns in the absence of sensory stimuli. To elucidate the nature of this ongoing activity, we present a theoretical treatment of two contrasting scenarios of cortical dynamics: (1) fluctuations about a single background state and (2) wandering among multiple “attractor” states, which encode a single or several stimulus features. Studying simplified network rate models of the primary visual cortex (V1), we show that the single state scenario is characterized by fast and high-dimensional Gaussian-like fluctuations, whereas in the multiple state scenario the fluctuations are slow, low dimensional, and highly non-Gaussian. Studying a more realistic model that incorporates correlations in the feedforward input, spatially restricted cortical interactions, and an experimentally derived layout of pinwheels, we show that recent optical-imaging data of ongoing activity in V1 are consistent with the presence of either a single background state or multiple attractor states encoding many features.
53
A threshold equation for action potential initiation (Platkiewicz & Brette 2010) 
 "We examined in models the influence of Na channel activation, inactivation, slow voltage-gated channels and synaptic conductances on spike threshold. We propose a threshold equation which quantifies the contribution of all these mechanisms. It provides an instantaneous time-varying value of the threshold, which applies to neurons with fluctuating inputs. ... We find that spike threshold depends logarithmically on Na channel density, and that Na channel inactivation and K channels can dynamically modulate it in an adaptive way: the threshold increases with membrane potential and after every action potential. " See paper for more.
54
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.
55
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.
56
A two-stage model of dendritic integration in CA1 pyramidal neurons (Katz et al. 2009) 
 "... In a two-stage integration model, inputs contribute directly to dendritic spikes, and outputs from multiple branches sum in the axon. ... We used serial-section electron microscopy to reconstruct individual apical oblique dendritic branches of CA1 pyramidal neurons and observe a synapse distribution consistent with the two-stage integration model. Computational modeling suggests that the observed synapse distribution enhances the contribution of each dendritic branch to neuronal output."
57
Accurate and fast simulation of channel noise in conductance-based model neurons (Linaro et al 2011) 
 We introduce and operatively present a general method to simulate channel noise in conductance-based model neurons, with modest computational overheads. Our approach may be considered as an accurate generalization of previous proposal methods, to the case of voltage-, ion-, and ligand-gated channels with arbitrary complexity. We focus on the discrete Markov process descriptions, routinely employed in experimental identification of voltage-gated channels and synaptic receptors.
58
ACh modulation in olfactory bulb and piriform cortex (de Almeida et al. 2013;Devore S, et al. 2014) 
 This matlab code was used in the papers de Almeida, Idiart and Linster, (2013), Devore S, de Almeida L, Linster C (2014) . This work uses a computational model of the OB and PC and their common cholinergic inputs to investigate how bulbar cholinergic modulation affects cortical odor processing.
59
Action Potential initiation and backpropagation in Neocortical L5 Pyramidal Neuron (Hu et al. 2009) 
 "...Previous computational studies have yielded conflicting conclusions about the role of Na+ channel density and biophysical properties in action potential initiation as a result of inconsistent estimates of channel density. Our modeling studies integrated the immunostaining and electrophysiological results and showed that the lowest threshold for action potential initiation at the distal AIS was largely determined by the density of low-threshold Nav1.6 channels ... Distinct from the function of Nav1.6 channel, the Nav1.2 channel may control action potential backpropagation because of its high density at the proximal AIS and high threshold. ... In conclusion, distal AIS accumulation of Nav1.6 channels determines the low threshold for action potential initiation; whereas proximal AIS accumulation of Nav1.2 channels sets the threshold for the generation of somatodendritic potentials and ensures action potential backpropagation to the soma and dendrites. Thus, Nav1.6 and Nav1.2 channels serve distinct functions in action potential initiation and backpropagation."
60
Action potential of adult rat ventricle (Wang et al. 2008) 
 "Aconitine (ACO), a highly toxic diterpenoid alkaloid, is recognized to have effects on cardiac voltage-gated Na(+) channels. However, it remains unknown whether it has any effects on K(+) currents. The effects of ACO on ion currents in differentiated clonal cardiac (H9c2) cells and in cultured neonatal rat ventricular myocytes were investigated in this study. ..." The rat action potential in this simulation was played back into the cell for experiments reported in this paper.
61
Action potential of striated muscel fiber (Adrian et al 1970) 
 1. Membrane currents during step depolarizations were determined by a method in which three electrodes were inserted near the end of a fibre in the frog's sartorius muscle. The theoretical basis and limitations of the method are discussed. 2. Measurements of the membrane capacity (CM) and resting resistance (RM) derived from the current during a step change in membrane potential are consistent with values found by other methods. 3. In fibres made mechanically inactive with hypertonic solutions (Ringer solution plus 350 mM sucrose) step depolarizations produced ionic currents which resembled those of nerve in showing (a) an early transient inward current, abolished by tetrodotoxin, which reversed when the depolarization was carried beyond an internal potential of about +20 mV, (b) a delayed outward current, with a linear instantaneous current¡Xvoltage relation, and a mean equilibrium potential with a normal potassium concentration (2¡P5 mM) of -85 mV. 4. The reversal potential for the early current appears to be consistent with the sodium equilibrium potential expected in hypertonic solutions. 5. The variation of the equilibrium potential for the delayed current (V¡¬K) with external potassium concentration suggests that the channel for delayed current has a ratio of potassium to sodium permeability of 30:1; this is less than the resting membrane where the ratio appears to be 100:1. V¡¬K corresponds well with the membrane potential at the beginning of the negative after-potential observed under similar conditions. 6. The variation of V¡¬K with the amount of current which has passed through the delayed channel suggests that potassium ions accumulate in a space of between 1/3 and 1/6 of the fibre volume. If potassium accumulates in the transverse tubular system (T system) much greater variation in V¡¬K would be expected. 7. The delayed current is not maintained but is inactivated like the early current. The inactivation is approximately exponential with a time constant of 0¡P5 to 1 sec at 20¢X C. The steady-state inactivation of the potassium current is similar to that for the sodium current, but its voltage dependence is less steep and the potential for half inactivation is 20 mV rate more positive. 8. Reconstructions of ionic currents were made in terms of the parameters (m, n, h) of the Hodgkin¡XHuxley model for the squid axon, using constants which showed a similar dependence on voltage. 9. Propagated action potentials and conduction velocities were computed for various conditions on the assumption that the T system behaves as if it were a series resistance and capacity in parallel with surface capacity and the channels for sodium, potassium and leak current. There was reasonable agreement with observed values, the main difference being that the calculated velocities and rates of rise were somewhat less than those observed experimentally.
62
Action potential reconstitution from measured current waveforms (Alle et al. 2009) 
 This NEURON code reconstitutes action potentials in a model of a hippocampal mossy fiber from experimentally measured sodium, potassium and calcium current waveforms as described in Alle et al. (2009).
63
Action potential-evoked Na+ influx are similar in axon and soma (Fleidervish et al. 2010) 
 "In cortical pyramidal neurons, the axon initial segment (AIS) is pivotal in synaptic integration. It has been asserted that this is because there is a high density of Na+ channels in the AIS. However, we found that action potential–associated Na+ flux, as measured by high-speed fluorescence Na+ imaging, was about threefold larger in the rat AIS than in the soma. Spike-evoked Na+ flux in the AIS and the first node of Ranvier was similar and was eightfold lower in basal dendrites. ... In computer simulations, these data were consistent with the known features of action potential generation in these neurons."
64
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. Fernanda.Saraga@utoronto.ca
65
Active dendritic action potential propagation (Casale & McCormick 2011) 
 This model explores the dendritic sodium and potassium conductances needed to recapitulate voltage-sensitive dye optical recordings of thalamic interneuron dendrites in the dorsal lateral geniculate nucleus. Model ion channels were selected based on pharmacological data.
66
Activity constraints on stable neuronal or network parameters (Olypher and Calabrese 2007) 
 "In this study, we developed a general description of parameter combinations for which specified characteristics of neuronal or network activity are constant. Our approach is based on the implicit function theorem and is applicable to activity characteristics that smoothly depend on parameters. Such smoothness is often intrinsic to neuronal systems when they are in stable functional states. The conclusions about how parameters compensate each other, developed in this study, can thus be used even without regard to the specific mathematical model describing a particular neuron or neuronal network. ..."
67
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. ... "
68
Activity dependent changes in motoneurones (Dai Y et al 2002, Gardiner et al 2002) 
 These two papers review various experimental papers and examine the effects of activity on motoneurons in a similar 5 compartment model with 10 active conductances. Included are slow (S) and fast (F) type and fast fatigue resistant (FR) and fast fatigable (FF) models corresponding to the types of motoneurons. See papers for more and details.
69
Activity dependent conductances in a neuron model (Liu et al. 1998) 
 "... We present a model of a stomatogastric ganglion (STG) neuron in which several Ca2+-dependent pathways are used to regulate the maximal conductances of membrane currents in an activity-dependent manner. Unlike previous models of this type, the regulation and modification of maximal conductances by electrical activity is unconstrained. The model has seven voltage-dependent membrane currents and uses three Ca2+ sensors acting on different time scales. ... The model suggests that neurons may regulate their conductances to maintain fixed patterns of electrical activity, rather than fixed maximal conductances, and that the regulation process requires feedback systems capable of reacting to changes of electrical activity on a number of different time scales."
70
Activity dependent regulation of pacemaker channels by cAMP (Wang et al 2002) 
 Demonstration of the physiological consequences of the cyclic allosteric gating scheme for Ih mediated by HCN2 in thalamocortical relay cells.
71
Adaptive exponential integrate-and-fire model (Brette & Gerstner 2005) 
 "We introduce a two-dimensional integrate-and-fire model that combines an exponential spike mechanism with an adaptation equation, based on recent theoretical findings. ... The model is especially reliable in high-conductance states, typical of cortical activity in vivo, in which intrinsic conductances were found to have a reduced role in shaping spike trains. These results are promising because this simple model has enough expressive power to reproduce qualitatively several electrophysiological classes described in vitro."
72
Adaptive robotic control driven by a versatile spiking cerebellar network (Casellato et al. 2014) 
 " ... We have coupled a realistic cerebellar spiking neural network (SNN) with a real robot and challenged it in multiple diverse sensorimotor tasks. ..."
73
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."
74
Alcohol excites Cerebellar Golgi Cells by inhibiting the Na+/K+ ATPase (Botta et al.2010) 
 Patch-clamp in cerebellar slices and computer modeling show that ethanol excites Golgi cells by inhibiting the Na+/K+ ATPase. In particular, voltage-clamp recordings of Na+/K+ ATPase currents indicated that ethanol partially inhibits this pump and this effect could be mimicked by low concentrations of the Na+/K+ ATPase blocker ouabain. The partial inhibition of Na+/K+ ATPase in a computer model of the Golgi cell reproduced these experimental findings that established a novel mechanism of action of ethanol on neural excitability.
75
Allosteric gating of K channels (Horrigan et al 1999) 
 Calcium sensitive large-conductance K channel conductance is controlled by both cytoplasmic calcium and membrane potential. Experimental data obtained by the inside out patch method can be understood in terms of a gating scheme where a central transition between a closed and an open conformation is allosterically regulated by the state of four independent and identical voltage sensors. See paper for more and details.
76
Altered complexity in layer 2/3 pyramidal neurons (Luuk van der Velden et al. 2012) 
 " ... Our experimental results show that hypercomplexity of the apical dendritic tuft of layer 2/3 pyramidal neurons affects neuronal excitability by reducing the amount of spike frequency adaptation. This difference in firing pattern, related to a higher dendritic complexity, was accompanied by an altered development of the afterhyperpolarization slope with successive action potentials. Our abstract and realistic neuronal models, which allowed manipulation of the dendritic complexity, showed similar effects on neuronal excitability and confirmed the impact of apical dendritic complexity. Alterations of dendritic complexity, as observed in several pathological conditions such as neurodegenerative diseases or neurodevelopmental disorders, may thus not only affect the input to layer 2/3 pyramidal neurons but also shape their firing pattern and consequently alter the information processing in the cortex."
77
Alternative time representation in dopamine models (Rivest et al. 2009) 
 Combines a long short-term memory (LSTM) model of the cortex to a temporal difference learning (TD) model of the basal ganglia. Code to run simulations similar to the published data: Rivest, F, Kalaska, J.F., Bengio, Y. (2009) Alternative time representation in dopamine models. Journal of Computational Neuroscience. See http://dx.doi.org/10.1007/s10827-009-0191-1 for details.
78
Ambiguous Encoding and Distorted Perception (Carlson and Kawasaki 2006) 
 "... In the weakly electric fish Eigenmannia, P- and T-type primary afferent fibers are specialized for encoding the amplitude and phase, respectively, of electrosensory stimuli. We used a stimulus estimation technique to quantify the ability of P- and T-units to encode random modulations in amplitude and phase. As expected, P-units exhibited a clear preference for encoding amplitude modulations, whereas T-units exhibited a clear preference for encoding phase modulations. Surprisingly, both types of afferents also encoded their nonpreferred stimulus attribute when it was presented in isolation or when the preferred stimulus attribute was sufficiently weak. Because afferent activity can be affected by modulations in either amplitude or phase, it is not possible to unambiguously distinguish between these two stimulus attributes by observing the activity of a single afferent fiber. Simple model neurons with a preference for encoding either amplitude or phase also encoded their nonpreferred stimulus attribute when it was presented in isolation, suggesting that such ambiguity is unavoidable. ... " See paper for more and details.
79
Amyloid beta (IA block) effects on a model CA1 pyramidal cell (Morse et al. 2010) 
 The model simulations provide evidence oblique dendrites in CA1 pyramidal neurons are susceptible to hyper-excitability by amyloid beta block of the transient K+ channel, IA. See paper for details.
80
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.
81
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.
82
An attractor network model of grid cells and theta-nested gamma oscillations (Pastoll et al., 2013) 
 A two population spiking continuous attractor model of grid cells. This model combines the attractor dynamics with theta-nested gamma oscillatory activity. It reproduces the behavioural response of grid cells (grid fields) in medial entorhinal cortex, while at the same time allowing for nested gamma oscillations of post-synaptic currents.
83
An integrative model of the cardiac ventricular myocytes (Greenstein and Winslow 2002) 
 The local control theory of excitation-contraction (EC) coupling in cardiac muscle asserts that L-type Ca2+ current tightly controls Ca2+ release from the sarcoplasmic reticulum (SR) via local interaction of closely apposed L-type Ca2+ channels (LCCs) and ryanodine receptors (RyRs). ...In this study we present a biophysically detailed model of the normal canine ventricular myocyte that conforms to local control theory. The model formulation incorporates details of microscopic EC coupling properties in the form of Ca2+ release units (CaRUs) in which individual sarcolemmal LCCs interact in a stochastic manner with nearby RyRs in localized regions ... See paper for more and details.
84
An oscillatory neural model of multiple object tracking (Kazanovich and Borisyuk 2006) 
 An oscillatory neural network model of multiple object tracking is described. The model works with a set of identical visual objects moving around the screen. At the initial stage, the model selects into the focus of attention a subset of objects initially marked as targets. Other objects are used as distractors. The model aims to preserve the initial separation between targets and distractors while objects are moving. This is achieved by a proper interplay of synchronizing and desynchronizing interactions in a multilayer network, where each layer is responsible for tracking a single target. The results of the model simulation are presented and compared with experimental data. In agreement with experimental evidence, simulations with a larger number of targets have shown higher error rates. Also, the functioning of the model in the case of temporarily overlapping objects is presented.
85
Analyzing neural time series data theory and practice (Cohen 2014) 
 "This book offers a comprehensive guide to the theory and practice of analyzing electrical brain signals. It explains the conceptual, mathematical, and implementational (via Matlab programming) aspects of time-, time-frequency- and synchronization-based analyses of magnetoencephalography (MEG), electroencephalography (EEG), and local field potential (LFP) recordings from humans and nonhuman animals."
86
AP back-prop. explains threshold variability and rapid rise (McCormick et al. 2007, Yu et al. 2008) 
 This simple axon-soma model explained how the rapid rising phase in the somatic spike is derived from the propagated axon initiated spike, and how the somatic spike threshold variance is affected by spike propagation.
87
AP initiation and propagation in type II cochlear ganglion cell (Hossain et al 2005) 
 The model of type II cochlear ganglion cell was based on the immunostaining of the mouse auditory pathway. Specific antibodies were used to map the distribution of voltage-dependent sodium channels along the two unmyelinated axon-like processes of the bipolar ganglion cells. Three distinct hot spots were detected. A high density of sodium channels was present over the entire trajectory of sensory endings beneath the outer hair cells (the most distal portion of the peripheral axon). THE other two hot spots were localized in the initial segments of both of the axons that flank the unmyelinated bipolar ganglion cell bodies.

A biophysical model indicates that all three hot spots might play important roles in action potential initiation and propagation. For instance, the hot spot in the receptor segment is important for transforming the receptor potentials into a full blown action potential (Supplemental Fig. 1). The hot spots in the two paraganglionic axon initial segments are there to ensure the successful propagation of action potentials from the peripheral to the central axon through the cell body.

The Readme.txt file provides step by step instructions on how to recreate Figures 6 and 7 of Hossain et al., 2005 paper.

88
AP shape and parameter constraints in optimization of compartment models (Weaver and Wearne 2006) 
 "... We construct an objective function that includes both time-aligned action potential shape error and errors in firing rate and firing regularity. We then implement a variant of simulated annealing that introduces a recentering algorithm to handle infeasible points outside the boundary constraints. We show how our objective function captures essential features of neuronal firing patterns, and why our boundary management technique is superior to previous approaches."
89
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
90
Application of Parker-Sochacki method to Hodgkin-Huxley equations (Wilanowski 2013, in review) 
 Reproduces figures 2-3 from Wilanowski, G. Integrating Hodgkin-Huxley equations (in review) The implementation of the giant squid axon and the Booth model with the Parker-Sochacki method combined with cubic splines interpolation of the Hodgkin-Huxley equations. Contact gwilanowski@ibib.waw.pl if you have any questions about the implementation of the model. Usage: 1. Unzip wilanowski2013.zip into empty directory. 2. Run simulation.m MATLAB script 3. A menu will appear that offers a selection of models (the giant squid axon or the Booth model 4. Choose 1 or 2 to reproduce the simulations shown Fig. 1 or Fig 2 of the original article. Only the Parker-Sochacki traces can be reproduced. Curve Fitting Toolbox is required. The program runs on Windows. It bases upon Numerical Integration of Izhikevich and HH model neurons (Stewart and Bair 2009) (http://senselab.med.yale.edu/modeldb/showmodel.asp?model=117361)
91
Arteriolar networks: Spread of potential (Crane et al 2001) 
 Crane, G.J., Hines, M.L., and Neild, T.O. (2001) Simulating the spread of membrane potential changes in arteriolar networks. Microcirculation 8:33-43. This model uses a gap junction density mechanism to couple arteriolar smooth muscle and endothelium in microvascular trees.
92
Artificial neuron model (Izhikevich 2003) 
 A model is presented that reproduces spiking and bursting behavior of known types of cortical neurons. The model combines the biologically plausibility of Hodgkin–Huxley-type dynamics and the computational efficiency of integrate-and-fire neurons. Using this model, one can simulate tens of thousands of spiking cortical neurons in real time (1 ms resolution) using a desktop PC.
93
Asynchronous irregular and up/down states in excitatory and inhibitory NNs (Destexhe 2009) 
 "Randomly-connected networks of integrate-and-fire (IF) neurons are known to display asynchronous irregular (AI) activity states, which resemble the discharge activity recorded in the cerebral cortex of awake animals. ... Here, we investigate the occurrence of AI states in networks of nonlinear IF neurons, such as the adaptive exponential IF (Brette-Gerstner-Izhikevich) model. This model can display intrinsic properties such as low-threshold spike (LTS), regular spiking (RS) or fast-spiking (FS). We successively investigate the oscillatory and AI dynamics of thalamic, cortical and thalamocortical networks using such models. ..."
94
Auditory cortex layer IV network model (Beeman 2013) 
 "... The primary objective of this modeling study was to determine the effects of axonal conduction velocity (often neglected, but significant), as well as synaptic time constants, on the ability of such a network to create and propagate cortical waves. ... The model is also being used to study the interaction between single and two-tone input and normal background activity, and the effects of synaptic depression from thalamic inputs. The simulation scripts have the additional purpose of serving as tutorial examples for the construction of cortical networks with GENESIS. The present model has fostered the development of the G-3 Python network analysis and visualization tools used in this study... It is my hope that this short tutorial and the example simulation scripts can provide a head start for a graduate student or postdoc who is beginning a cortical modeling project. "
95
Auditory nerve model for predicting performance limits (Heinz et al 2001) 
 A computational auditory nerve (AN) model was developed for use in modeling psychophysical experiments with normal and impaired human listeners. In this phenomenological model, many physiologically vulnerable response properties associated with the cochlear amplifier are represented by a single nonlinear control mechanism, see paper for details. Several model versions are described that can be used to evaluate the relative effects of these nonlinear properties.
96
Auditory nerve model with linear tuning (Heinz et al 2001) 
 A method for calculating psychophysical performance limits based on stochastic neural responses is introduced and compared to previous analytical methods for evaluating auditory discrimination of tone frequency and level. The method uses signal detection theory and a computational model for a population of auditory nerve (AN) fiber responses. Please see paper for details.
97
Auditory nerve response model (Tan, Carney 2003) 
 A computational model was developed to simulate the responses of auditory-nerve (AN) fibers in cat. The incorporation of both the level-independent frequency glide and the level-dependent compressive nonlinearity into a phenomenological model for the AN was the primary focus of this work. The ability of this model to process arbitrary sound inputs makes it a useful tool for studying peripheral auditory processing.
98
Auditory nerve response model (Zhang et al 2001) 
 A phenomenological model was developed to describe responses of high-spontaneous-rate auditory-nerve (AN) fibers, including several nonlinear response properties. The implementation of this model represents a relatively simple phenomenological description of a single mechanism that underlies several important nonlinear response properties of AN fibers. The model provides a tool for studying the roles of these nonlinearities in the encoding of simple and complex sounds in the responses of populations of AN fibers.
99
Auditory nerve spontaneous rate histograms (Jackson and Carney 2005) 
 Histograms of spontaneous rate estimates of auditory nerve are well reproduced by models with two or three spontaneous rates and long range dependence.
100
Availability of low-threshold Ca2+ current in retinal ganglion cells (Lee SC et al. 2003) 
 "... we measured T-type current of isolated goldfish retinal ganglion cells with perforated-patch voltageclamp methods in solutions containing a normal extracellular Ca2+ concentration. The voltage sensitivities and rates of current activation, inactivation, deactivation, and recovery from inactivation were similar to those of expressed +1G (CaV3.1) Ca2+ channel clones, except that the rate of deactivation was significantly faster. We reproduced the amplitude and kinetics of measured T currents with a numerical simulation based on a kinetic model developed for an +1G Ca2+ channel. Finally, we show that this model predicts the increase of T-type current made available between resting potential and spike threshold by repetitive hyperpolarizations presented at rates that are within the bandwidth of signals processed in situ by these neurons."
Yale Center for Medical Informatics Questions, comments, problems? Email the Database Administrator
This site is Copyright 2014, Yale Center for Medical Informatics