Circuits that contain the Neurotransmitter : Gaba

Re-display model names without descriptions

	Models	Description
1.	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. ..."
2.	A cortico-cerebello-thalamo-cortical loop model under essential tremor (Zhang & Santaniello 2019)
		We investigated the origins of oscillations under essential tremor (ET) by building a computational model of the cortico-cerebello-thalamo-cortical loop. It showed that an alteration of amplitudes and decay times of the GABAergic currents to the dentate nucleus can facilitate sustained oscillatory activity at tremor frequency throughout the network as well as a robust bursting activity in the thalamus, which is consistent with observations of thalamic tremor cells in ET patients. Tremor-related oscillations initiated in small neural populations and spread to a larger network as the synaptic dysfunction increased, while thalamic high-frequency stimulation suppressed tremor-related activity in thalamus but increased the oscillation frequency in the olivocerebellar loop.
3.	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.
4.	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.
5.	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.
6.	A neural mass model for critical assessment of brain connectivity (Ursino et al 2020)
		We use a neural mass model of interconnected regions of interest to simulate reliable neuroelectrical signals in the cortex. In particular, signals simulating mean field potentials were generated assuming two, three or four ROIs, connected via excitatory or by-synaptic inhibitory links. Then we investigated whether bivariate Transfer Entropy (TE) can be used to detect a statistically significant connection from data (as in binary 0/1 networks), and even if connection strength can be quantified (i.e., the occurrence of a linear relationship between TE and connection strength). Results suggest that TE can reliably estimate the strength of connectivity if neural populations work in their linear regions. However, nonlinear phenomena dramatically affect the assessment of connectivity, since they may significantly reduce TE estimation. Software included here allows the simulation of neural mass models with a variable number of ROIs and connections, the estimation of TE using the free package Trentool, and the realization of figures to compare true connectivity with estimated values.
7.	A NN with synaptic depression for testing the effects of connectivity on dynamics (Jacob et al 2019)
		Here we used a 10,000 neuron model. The neurons are a mixture of excitatory and inhibitory integrate-and-fire neurons connected with synapses that exhibit synaptic depression. Three different connectivity paradigms were tested to look for spontaneous transition between interictal spiking and seizure: uniform, small-world network, and scale-free. All three model types are included here.
8.	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.
9.	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.
10.	Basal ganglia network model of subthalamic deep brain stimulation (Hahn and McIntyre 2010)
		Basal ganglia network model of parkinsonian activity and subthalamic deep brain stimulation in non-human primates from the article Instructions are provided in the README.txt file. Contact hahnp@ccf.org if you have any questions about the implementation of the model. Please include "ModelDB - BGnet" in the subject heading.
11.	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.
12.	Biophysically Realistic Network Model of the Wild-Type and Degenerate Retina (Ly et al 2022)
		Please read the readme.txt file before running any code. Objective: A major reason for poor visual outcomes provided by existing retinal prostheses is the limited knowledge of the impact of photoreceptor loss on retinal remodelling and its subsequent impact on neural responses to electrical stimulation. Computational network models of the neural retina assist in the understanding of normal retinal function but can be also useful for investigating diseased retinal responses to electrical stimulation. Approach: We developed and validated a biophysically detailed discrete neuronal network model of the retina in the software package NEURON. The model includes rod and cone photoreceptors, ON and OFF bipolar cell pathways, amacrine and horizontal cells and finally, ON and OFF retinal ganglion cells with detailed network connectivity and neural intrinsic properties. By accurately controlling the network parameters, we simulated the impact of varying levels of degeneration on retinal electrical function. Main results: Our model was able to reproduce characteristic monophasic and biphasic oscillatory patterns seen in ON and OFF neurons during retinal degeneration. Oscillatory activity occurred at 3 Hz with partial photoreceptor loss and at 6 Hz when all photoreceptor input to the retina was removed. Oscillations were found to gradually weaken, then disappear when synapses and gap junctions were destroyed in the inner retina. Without requiring any changes to intrinsic cellular properties of individual inner retinal neurons, our results suggest that changes in connectivity alone were sufficient to give rise to neural oscillations during photoreceptor degeneration, and significant network connectivity destruction in the inner retina terminated the oscillations. Significance: Our results provide a platform for further understanding physiological retinal changes with progressive photoreceptor and inner retinal degeneration. Furthermore, our model can be used to guide future stimulation strategies for retinal prostheses to benefit patients at different stages of disease progression, particularly in the early and mid-stages of retinal degeneration.
13.	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. ..."
14.	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.
15.	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.
16.	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.
17.	Competition model of pheromone ratio detection (Zavada et al. 2011)
		For some closely related sympatric moth species, recognizing a specific pheromone component concentration ratio is essential for mating success. We propose and test a minimalist competition-based feed-forward neuronal model capable of detecting a certain ratio of pheromone components independently of overall concentration. This model represents an elementary recognition unit for binary mixtures which we propose is entirely contained in the macroglomerular complex (MGC) of the male moth. A set of such units, along with projection neurons (PNs), can provide the input to higher brain centres. We found that (1) accuracy is mainly achieved by maintaining a certain ratio of connection strengths between olfactory receptor neurons (ORN) and local neurons (LN), much less by properties of the interconnections between the competing LNs proper. (2) successful ratio recognition is achieved using latency-to-first-spike in the LN populations which. (3) longer durations of the competition process between LNs did not result in higher recognition accuracy.
18.	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.
19.	Computer model of clonazepam's effect in thalamic slice (Lytton 1997)
		Demonstration of the effect of a minor pharmacological synaptic change at the network level. Clonazepam, a benzodiazepine, enhances inhibition but is paradoxically useful for certain types of seizures. This simulation shows how inhibition of inhibitory cells (the RE cells) produces this counter-intuitive effect.
20.	Conductance-based model of Layer-4 in the barrel cortex (Argaman et Golomb 2017)
		Layer 4 in the mouse barrel cortex includes hundreds of inhibitory PV neurons and thousands of excitatory neurons. Despite this fact, its dynamical state is similar to a balanced state of large neuronal circuits.
21.	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. ..."
22.	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.
23.	Cortical Interneuron & Pyramidal Cell Model of Cortical Spreading Depression (Stein & Harris 2022)
		This 2-cell cortical circuit model consists of a negative feedback loop between a single compartment pyramidal cell and a single compartment interneuron. Ion concentrations in the extra- and intracellular spaces are included in the model. The model is used to test the contribution of cortical inhibitory interneurons to the initiation of cortical spreading depression, as characterized by spike block in the pyramidal cell. Results show that interneuronal inhibition provides a wider dynamic range to the circuit and generally improves stability against spike block. Despite these beneficial effects, strong interneuronal firing contributed to rapidly changing extracellular ion concentrations, which facilitated hyperexcitation and led to spike block first in the interneuron and then in the pyramidal cell. The model results demonstrate that while the role of interneurons in cortical microcircuits is complex, they are critical to the initiation of pyramidal cell spike block and CSD. See reference below for more details.
24.	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.
25.	CRH modulates excitatory transmission and network physiology in hippocampus (Gunn et al. 2017)
		This model simulates the effects of CRH on sharp waves in a rat CA1/CA3 model. It uses the frequency of the sharp waves as an output of the network.
26.	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."
27.	Default mode network model (Matsui et al 2014)
		Default mode network (DMN) shows intrinsic, high-level activity at rest. We tested a hypothesis proposed for its role in sensory information processing: Intrinsic DMN activity facilitates neural responses to sensory input. A neural network model, consisting of a sensory network (Nsen) and a DMN, was simulated. The Nsen contained cell assemblies. Each cell assembly comprised principal cells, GABAergic interneurons (Ia, Ib), and glial cells. We let the Nsen carry out a perceptual task: detection of sensory stimuli. … This enabled the Nsen to reliably detect the stimulus. We suggest that intrinsic default model network activity may accelerate the reaction speed of the sensory network by modulating its ongoing-spontaneous activity in a subthreshold manner. Ambient GABA contributes to achieve an optimal ongoing spontaneous subthreshold neuronal state, in which GABAergic gliotransmission triggered by the intrinsic default model network activity may play an important role.
28.	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.
29.	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. ...
30.	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).
31.	Distance-dependent inhibition in the hippocampus (Strüber et al. 2017)
		Network model of a hippocampal circuit including interneurons and principal cells. Amplitude and decay time course of inhibitory synapses can be systematically changed for different distances between connected cells. Various forms of excitatory drives can be administered to the network including spatially structured input.
32.	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>
33.	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).
34.	Dynamic dopamine modulation in the basal ganglia: Learning in Parkinson (Frank et al 2004,2005)
		See README file for all info on how to run models under different tasks and simulated Parkinson's and medication conditions.
35.	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.
36.	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. ... "
37.	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. ..."
38.	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.
39.	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."
40.	Functional balanced networks with synaptic plasticity (Sadeh et al, 2015)
		The model investigates the impact of learning on functional sensory networks. It uses large-scale recurrent networks of excitatory and inhibitory spiking neurons equipped with synaptic plasticity. It explains enhancement of orientation selectivity and emergence of feature-specific connectivity in visual cortex of rodents during development, as reported in experiments.
41.	Gap junction coupled network of striatal fast spiking interneurons (Hjorth et al. 2009)
		Gap junctions between striatal FS neurons has very weak ability to synchronise spiking. Input uncorrelated between neighbouring neurons is shunted, while correlated input is not.
42.	Gap junction plasticity as a mechanism to regulate network-wide oscillations (Pernelle et al 2018)
		"Oscillations of neural activity emerge when many neurons repeatedly activate together and are observed in many brain regions, particularly during sleep and attention. Their functional role is still debated, but could be associated with normal cognitive processes such as memory formation or with pathologies such as schizophrenia and autism. Powerful oscillations are also a hallmark of epileptic seizures. Therefore, we wondered what mechanism could regulate oscillations. A type of neuronal coupling, called gap junctions, has been shown to promote synchronization between inhibitory neurons. Computational models show that when gap junctions are strong, neurons synchronize together. Moreover recent investigations show that the gap junction coupling strength is not static but plastic and dependent on the firing properties of the neurons. Thus, we developed a model of gap junction plasticity in a network of inhibitory and excitatory neurons. We show that gap junction plasticity can maintain the right amount of oscillations to prevent pathologies from emerging. Finally, we show that gap junction plasticity serves an additional functional role and allows for efficient and robust information transfer."
43.	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.
44.	Hippocampal basket cell gap junction network dynamics (Saraga et al. 2006)
		2 cell network of hippocampal basket cells connected by gap junctions. Paper explores how distal gap junctions and active dendrites can tune network dynamics.
45.	Hippocampal CA1 NN with spontaneous theta, gamma: full scale & network clamp (Bezaire et al 2016)
		This model is a full-scale, biologically constrained rodent hippocampal CA1 network model that includes 9 cells types (pyramidal cells and 8 interneurons) with realistic proportions of each and realistic connectivity between the cells. In addition, the model receives realistic numbers of afferents from artificial cells representing hippocampal CA3 and entorhinal cortical layer III. The model is fully scaleable and parallelized so that it can be run at small scale on a personal computer or large scale on a supercomputer. The model network exhibits spontaneous theta and gamma rhythms without any rhythmic input. The model network can be perturbed in a variety of ways to better study the mechanisms of CA1 network dynamics. Also see online code at http://bitbucket.org/mbezaire/ca1 and further information at http://mariannebezaire.com/models/ca1
46.	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).
47.	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.
48.	Human layer 2/3 cortical microcircuits in health and depression (Yao et al, 2022)
49.	Human sleep/wake cycle (Rempe et al. 2010)
		This model simulates sleep in the human brain and is consistent with both the flip/flop concept and the two-process model of sleep regulation. The model also gives a possible mechanism for the changes in sleep timing seen in narcolepsy.
50.	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
51.	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. ..."
52.	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"
53.	Inferior Olive, subthreshold oscillations (Torben-Nielsen, Segev, Yarom 2012)
		The Inferior Olive is a brain structure in which neurons are solely connected to each other through gap-junctions. Its behavior is characterized by spontaneous subthreshold oscillation, frequency changes in the subthreshold oscillation, stable phase differences between neurons, and propagating waves of activity. Our model based on actual IO topology can reproduce these behaviors and provides a mechanistic explanation thereof.
54.	Inhibition and glial-K+ interaction leads to diverse seizure transition modes (Ho & Truccolo 2016)
		"How focal seizures initiate and evolve in human neocortex remains a fundamental problem in neuroscience. Here, we use biophysical neuronal network models of neocortical patches to study how the interaction between inhibition and extracellular potassium ([K+]o) dynamics may contribute to different types of focal seizures. Three main types of propagated focal seizures observed in recent intracortical microelectrode recordings in humans were modelled ..."
55.	Ketamine disrupts theta modulation of gamma in a computer model of hippocampus (Neymotin et al 2011)
		"Abnormalities in oscillations have been suggested to play a role in schizophrenia. We studied theta-modulated gamma oscillations in a computer model of hippocampal CA3 in vivo with and without simulated application of ketamine, an NMDA receptor antagonist and psychotomimetic. Networks of 1200 multi-compartment neurons (pyramidal, basket and oriens-lacunosum moleculare, OLM, cells) generated theta and gamma oscillations from intrinsic network dynamics: basket cells primarily generated gamma and amplified theta, while OLM cells strongly contributed to theta. ..."
56.	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 .
57.	L4 cortical barrel NN model receiving thalamic input during whisking or touch (Gutnisky et al. 2017)
		Excitatory neurons in layer 4 (L4) in the barrel cortex respond relatively strongly to touch but not to whisker movement (Yu et al., Nat. Neurosci. 2016). The model explains the mechanism underlying this effect. The network is settled to filter out most stationary inputs. Brief touch input passes through because it takes time until feed-forward inhibition silences excitatory neurons receiving brief and strong thalamic excitation.
58.	Large scale model of the olfactory bulb (Yu et al., 2013)
		The readme file currently contains links to the results for all the 72 odors investigated in the paper, and the movie showing the network activity during learning of odor k3-3 (an aliphatic ketone).
59.	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. ..."
60.	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
61.	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."
62.	Locust olfactory network with GGN and full KC population in the mushroom body (Ray et al 2020)
		We reconstructed the GGN (giant GABAergic neuron) morphology from 3D confocal image stack, and built a passive model based on the morphology to study signal attenuation across this giant neuron. In order to study the effect of feedback inhibition from this cell on odor information processing, we created a model of the olfactory network in the locust mushroom body with 50,000 KCs (Kenyon cell) reciprocally connected to this neuron. Finally, we added a model of the IG (Inhibitor of GGN) to reproduce in vivo odor responses in GGN.
63.	Logarithmic distributions prove that intrinsic learning is Hebbian (Scheler 2017)
		"In this paper, we present data for the lognormal distributions of spike rates, synaptic weights and intrinsic excitability (gain) for neurons in various brain areas, such as auditory or visual cortex, hippocampus, cerebellum, striatum, midbrain nuclei. We find a remarkable consistency of heavy-tailed, specifically lognormal, distributions for rates, weights and gains in all brain areas examined. The difference between strongly recurrent and feed-forward connectivity (cortex vs. striatum and cerebellum), neurotransmitter (GABA (striatum) or glutamate (cortex)) or the level of activation (low in cortex, high in Purkinje cells and midbrain nuclei) turns out to be irrelevant for this feature. Logarithmic scale distribution of weights and gains appears to be a general, functional property in all cases analyzed. We then created a generic neural model to investigate adaptive learning rules that create and maintain lognormal distributions. We conclusively demonstrate that not only weights, but also intrinsic gains, need to have strong Hebbian learning in order to produce and maintain the experimentally attested distributions. This provides a solution to the long-standing question about the type of plasticity exhibited by intrinsic excitability."
64.	MEC PV-positive fast-spiking interneuron network generates theta-nested fast oscillations
		We use a computational model of a network of Fast-Spiking Parvalbumin-positive Basket Cells to study its synchronizing properties. The intrinsic properties of neurons, properties of chemical synapses and of gap junctions are calibrated using electrophysiological recordings in mice Medial Entorhinal Cortex slices. The neurons synchronize, generating Fast Oscillations nested in an external theta drive. We show how gap junctions are necessary for the generation of the oscillations, how hyperpolarizing chemical synapses give rise to more robust fast oscillations, compared to shunting ones, and how short-term depression in the chemical synapses confine the fast oscillation on a narrow range of phases from the external theta drive.
65.	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."
66.	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. "
67.	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. ..."
68.	Motor Cortex Connectivity & Event Related Desynchronization Based on Neural Mass Models (Ursino 21)
		Knowledge of motor cortex connectivity is of great value in cognitive neuroscience, in order to provide a better understanding of motor organization and its alterations in pathological conditions. Traditional methods provide connectivity estimations which may vary depending on the task. This work aims to propose a new method for motor connectivity assessment based on the hypothesis of a task-independent connectivity network, assuming nonlinear behavior. The model considers six cortical regions of interest (ROIs) involved in hand movement. The dynamics of each region is simulated using a neural mass model, which reproduces the oscillatory activity through the interaction among four neural populations. Parameters of the model have been assigned to simulate both power spectral densities and coherences of a patient with left-hemisphere stroke during: resting condition, movement of the affected and movement of the unaffected hand. The presented model can simulate the three conditions using a single set of connectivity parameters, assuming that only inputs to the ROIs change from one condition to the other. The proposed procedure represents an innovative method to assess a brain circuit, which does not rely on a task-dependent connectivity network, and allows brain rhythms and desynchronization to be assessed on a quantitative basis.
69.	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."
70.	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 ..."
71.	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.
72.	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.
73.	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. ..."
74.	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)
75.	Neural Mass Model for relationship between Brain Rhythms + Functional Connectivity (Ricci et al '21)
		The Neural Mass Model (NMM) generates biologically reliable mean field potentials of four interconnected regions of interest (ROIs) of the cortex, each simulating a different brain rhythm (in theta, alpha, beta and gamma ranges). These neuroelectrical signals originate from the assumption that ROIs influence each other via of excitatory or by-synaptic inhibitory connections. Besides receiving long-range synapses from other ROIs, each one receives an external input and superimposed Gaussian white noise. We used the NMM to simulate different connectivity networks of four ROIs, by varying both the synaptic strengths and the inputs. The purpose of this study is to investigate how the transmission of brain rhythms behaves under linear and nonlinear conditions. To this aim, we investigated the performance of eight Functional Connectivity (FC) estimators (Correlation, Delayed Correlation, Coherence, Lagged Coherence, Temporal Granger Causality, Spectral Granger Causality, Phase Synchronization and Transfer Entropy) in detecting the connectivity network changes. Results suggest that when a ROI works in the linear region, its capacity to transmit its rhythm increases, while when it saturates, the oscillatory activity becomes strongly affected by other ROIs. Software included here allows the simulation of mean field potentials of four interconnected ROIs, their visualization, both in time and frequency domains, and the estimation of the related FC with eight different methods (for Transfer Entropy the Trentool package is needed).
76.	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."
77.	Noise promotes independent control of gamma oscillations and grid firing (Solanka et al 2015)
		"Neural computations underlying cognitive functions require calibration of the strength of excitatory and inhibitory synaptic connections and are associated with modulation of gamma frequency oscillations in network activity. However, principles relating gamma oscillations, synaptic strength and circuit computations are unclear. We address this in attractor network models that account for grid firing and theta-nested gamma oscillations in the medial entorhinal cortex. ..."
78.	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
79.	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.
80.	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.
81.	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.
82.	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.
83.	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.
84.	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.
85.	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.
86.	Optimal deep brain stimulation of the subthalamic nucleus-a computational study (Feng et al. 2007)
		Here, we use a biophysically-based model of spiking cells in the basal ganglia (Terman et al., Journal of Neuroscience, 22, 2963-2976, 2002; Rubin and Terman, Journal of Computational Neuroscience, 16, 211-235, 2004) to provide computational evidence that alternative temporal patterns of DBS inputs might be equally effective as the standard high-frequency waveforms, but require lower amplitudes. Within this model, DBS performance is assessed in two ways. First, we determine the extent to which DBS causes Gpi (globus pallidus pars interna) synaptic outputs, which are burstlike and synchronized in the unstimulated Parkinsonian state, to cease their pathological modulation of simulated thalamocortical cells. Second, we evaluate how DBS affects the GPi cells' auto- and cross-correlograms.
87.	Orientation selectivity in inhibition-dominated recurrent networks (Sadeh and Rotter, 2015)
		Emergence of contrast-invariant orientation selectivity in large-scale networks of excitatory and inhibitory neurons using integrate-and-fire neuron models.
88.	Oscillations emerging from noise-driven NNs (Tchumatchenko & Clopath 2014)
		" ... Here we show how the oscillation frequency is shaped by single neuron resonance, electrical and chemical synapses.The presence of both gap junctions and subthreshold resonance are necessary for the emergence of oscillations. Our results are in agreement with several experimental observations such as network responses to oscillatory inputs and offer a much-needed conceptual link connecting a collection of disparate effects observed in networks."
89.	Pallidostriatal projections promote beta oscillations (Corbit, Whalen, et al 2016)
		This model consists of an inhibitory loop combining the projections from GPe neurons back to the striatum (shown experimentally to predominantly affect fast spiking interneurons, FSIs), together with the coupling from FSIs to medium spiny neurons (MSNs) in the striatum, along with the projections from MSNs to GPe. All models are in the Hodgkin-Huxley formalism, adapted from previously published models for each cell type. The connected circuit produces irregular activity under control conditions, but increasing FSI-to-MSN connectivity as observed experimentally under dopamine depletion yields exaggerated beta oscillations and synchrony. Additional mechanistic aspects are also explored.
90.	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. [...]"
91.	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.
92.	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. ..."
93.	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.
94.	Piriform cortex network model with multicompartment neurons for cell assemblies (Traub et al 2021)
		Model contains layer 2 and layer 3 pyr cells, semilunar cells, and multiple types of superficial and deep interneurons. Used to investigate how cell assemblies and sharp waves emerge in different parameter regimes. Correlated with experimental recording. May be useful to better understand how the structure could be used for associative memory.
95.	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.
96.	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
97.	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.
98.	Reconstrucing sleep dynamics with data assimilation (Sedigh-Sarvestani et al., 2012)
		We have developed a framework, based on the unscented Kalman filter, for estimating hidden states and parameters of a network model of sleep. The network model includes firing rates and neurotransmitter output of 5 cell-groups in the rat brain.
99.	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.
100.	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. ..."
101.	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.
102.	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.
103.	Role for short term plasticity and OLM cells in containing spread of excitation (Hummos et al 2014)
		This hippocampus model was developed by matching experimental data, including neuronal behavior, synaptic current dynamics, network spatial connectivity patterns, and short-term synaptic plasticity. Furthermore, it was constrained to perform pattern completion and separation under the effects of acetylcholine. The model was then used to investigate the role of short-term synaptic depression at the recurrent synapses in CA3, and inhibition by basket cell (BC) interneurons and oriens lacunosum-moleculare (OLM) interneurons in containing the unstable spread of excitatory activity in the network.
104.	Roles of subthalamic nucleus and DBS in reinforcement conflict-based decision making (Frank 2006)
		Deep brain stimulation (DBS) of the subthalamic nucleus dramatically improves the motor symptoms of Parkinson's disease, but causes cognitive side effects such as impulsivity. This model from Frank (2006) simulates the role of the subthalamic nucleus (STN) within the basal ganglia circuitry in decision making. The STN dynamically modulates network decision thresholds in proportion to decision conflict. The STN ``hold your horses'' signal adaptively allows the system more time to settle on the best choice when multiple options are valid. The model also replicates effects in Parkinson's patients on and off DBS in experiments designed to test the model (Frank et al, 2007).
105.	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. ... "
106.	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.
107.	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. ..."
108.	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
109.	Spiking neuron model of the basal ganglia (Humphries et al 2006)
		A spiking neuron model of the basal ganglia (BG) circuit (striatum, STN, GP, SNr). Includes: parallel anatomical channels; tonic dopamine; dopamine receptors in striatum, STN, and GP; burst-firing in STN; GABAa, AMPA, and NMDA currents; effects of synaptic location. Model demonstrates selection and switching of input signals. Replicates experimental data on changes in slow-wave (<1 Hz) and gamma-band oscillations within BG nuclei following lesions and pharmacological manipulations.
110.	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.
111.	Status epilepticus alters dentate basket cell tonic inhibition (Yu J et al 2013)
		Status epilepticus (SE) leads to changes in dentate inhibitory neuronal networks and alters synaptic and tonic inhibition in granule cells. Recently, we identified that one week after pilocarpine-induced status epilepticus, dentate fast-spiking basket cells (FS-BCs), which underlie fast perisomatic inhibition, show two distinct changes in inhibition: (1) enhanced tonic currents (IGABA) and (2)depolarizing shift in GABA reversal (EGABA) following SE. These two changes can have opposing effects on neuronal inhibition with increases in tonic GABA conductance (gGABA) reducing excitability when the GABA currents are shunting (or hyperpolarizing) and potentially enhancing excitability when GABA currents are depolarizing. The following model is used to examine the post-SE changes in tonic GABA conductance, together with the depolarized GABA reversal potential modify FS-BC excitability and dentate network activity.
112.	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..."
113.	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.
114.	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.
115.	Striatal NN model of MSNs and FSIs investigated effects of dopamine depletion (Damodaran et al 2015)
		This study investigates the mechanisms that are affected in the striatal network after dopamine depletion and identifies potential therapeutic targets to restore normal activity.
116.	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.
117.	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.
118.	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.
119.	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..."
120.	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.
121.	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.
122.	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."
123.	Thalamic quiescence of spike and wave seizures (Lytton et al 1997)
		A phase plane analysis of a two cell interaction between a thalamocortical neuron (TC) and a thalamic reticularis neuron (RE).
124.	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.
125.	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. ..."
126.	Theta-gamma phase amplitude coupling in a hippocampal CA1 microcircuit (Ponzi et al. 2023)
		Using a data-driven model of a hippocampal microcircuit, we demonstrate that theta-gamma phase amplitude coupling (PAC) can naturally emerge from a single feedback mechanism involving an inhibitory and excitatory neuron population, which interplay to generate theta frequency periodic bursts of higher frequency gamma..
127.	Vertical System (VS) tangential cells network model (Trousdale et al. 2014)
		Network model of the VS tangential cell system, with 10 cells per hemisphere. Each cell is a two compartment model with one compartment for dendrites and one for the axon. The cells are coupled through axonal gap junctions. The code allows to simulate responses of the VS network to a variety of visual stimuli to investigate coding as a function of gap junction strength.

Re-display model names without descriptions