Models that contain the Cell : Neocortex fast spiking (FS) interneuron

(These are the majority of inhibitory interneurons in the neocortex (e.g. Golomb et al. 2007))
Re-display model names without descriptions
    Models   Description
1.  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.
2.  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.
3.  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. ..."
4.  Collection of simulated data from a thalamocortical network model (Glabska, Chintaluri, Wojcik 2017)
"A major challenge in experimental data analysis is the validation of analytical methods in a fully controlled scenario where the justification of the interpretation can be made directly and not just by plausibility. ... One solution is to use simulations of realistic models to generate ground truth data. In neuroscience, creating such data requires plausible models of neural activity, access to high performance computers, expertise and time to prepare and run the simulations, and to process the output. To facilitate such validation tests of analytical methods we provide rich data sets including intracellular voltage traces, transmembrane currents, morphologies, and spike times. ... The data were generated using the largest publicly available multicompartmental model of thalamocortical network (Traub et al. 2005), with activity evoked by different thalamic stimuli."
5.  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.
6.  Computational Surgery (Lytton et al. 2011)
Figure 2 in Neocortical simulation for epilepsy surgery guidance: Localization and intervention, by William W. Lytton, Samuel A. Neymotin, Jason C. Wester, and Diego Contreras in Computational Surgery and Dual Training, Springer, 2011
7.  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.
8.  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."
9.  Dynamic cortical interlaminar interactions (Carracedo et al. 2013)
"... Here we demonstrate the mechanism underlying a purely neocortical delta rhythm generator and show a remarkable laminar, cell subtype and local subcircuit delineation between delta and nested theta rhythms. We show that spike timing during delta-nested theta rhythms controls an iterative, reciprocal interaction between deep and superficial cortical layers resembling the unsupervised learning processes proposed for laminar neural networks by Hinton and colleagues ... and mimicking the alternating cortical dynamics of sensory and memory processing during wakefulness."
10.  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. ... "
11.  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. ..."
12.  Engaging distinct oscillatory neocortical circuits (Vierling-Claassen et al. 2010)
"Selective optogenetic drive of fast-spiking (FS) interneurons (INs) leads to enhanced local field potential (LFP) power across the traditional “gamma” frequency band (20–80 Hz; Cardin et al., 2009). In contrast, drive to regular-spiking (RS) pyramidal cells enhances power at lower frequencies, with a peak at 8 Hz. The first result is consistent with previous computational studies emphasizing the role of FS and the time constant of GABAA synaptic inhibition in gamma rhythmicity. However, the same theoretical models do not typically predict low-frequency LFP enhancement with RS drive. To develop hypotheses as to how the same network can support these contrasting behaviors, we constructed a biophysically principled network model of primary somatosensory neocortex containing FS, RS, and low-threshold spiking (LTS) INs. ..."
13.  Extraction and classification of three cortical neuron types (Mensi et al. 2012)
This script proposes a new convex fitting procedure that allows the parameters estimation of a large class of stochastic Integrate-and-Fire model upgraded with spike-triggered current and moving threshold from patch-clamp experiments (i.e. given the injected current and the recorded membrane potential). This script applies the method described in the paper to estimate the parameters of a reference model from a single voltage trace and the corresponding input current and evaluate the performance of the fitted model on a separated test set.
14.  Fast-spiking cortical interneuron (Golomb et al. 2007)
Cortical fast-spiking (FS) interneurons display highly variable electrophysiological properties. We hypothesize that this variability emerges naturally if one assumes a continuous distribution of properties in a small set of active channels. We construct a minimal, single-compartment conductance-based model of FS cells that includes transient Na+, delayed-rectifier K+, and slowly inactivating d-type K+ conductances. The model may display delay to firing. Stuttering (elliptic bursting) and subthreshold oscillations may be observed for small Na+ window current.
15.  Gamma and theta rythms in biophysical models of hippocampus circuits (Kopell et al. 2011)
" ... the main rhythms displayed by the hippocampus, the gamma (30–90 Hz) and theta (4–12 Hz) rhythms. We concentrate on modeling in vitro experiments, but with an eye toward possible in vivo implications. ... We use simpler biophysical models; all cells have a single compartment only, and the interneurons are restricted to two types: fast-spiking (FS) basket cells and oriens lacunosum-moleculare (O-LM) cells. ... , we aim not so much at reproducing dynamics in great detail, but at clarifying the essential mechanisms underlying the production of the rhythms and their interactions (Kopell, 2005). ..."
16.  Hodgkin-Huxley models of different classes of cortical neurons (Pospischil et al. 2008)
"We review here the development of Hodgkin- Huxley (HH) type models of cerebral cortex and thalamic neurons for network simulations. The intrinsic electrophysiological properties of cortical neurons were analyzed from several preparations, and we selected the four most prominent electrophysiological classes of neurons. These four classes are 'fast spiking', 'regular spiking', 'intrinsically bursting' and 'low-threshold spike' cells. For each class, we fit 'minimal' HH type models to experimental data. ..."
17.  Knox implementation of Destexhe 1998 spike and wave oscillation model (Knox et al 2018)
" ...The aim of this study was to use an established thalamocortical computer model to determine how T-type calcium channels work in concert with cortical excitability to contribute to pathogenesis and treatment response in CAE. METHODS: The model is comprised of cortical pyramidal, cortical inhibitory, thalamocortical relay, and thalamic reticular single-compartment neurons, implemented with Hodgkin-Huxley model ion channels and connected by AMPA, GABAA , and GABAB synapses. Network behavior was simulated for different combinations of T-type calcium channel conductance, inactivation time, steady state activation/inactivation shift, and cortical GABAA conductance. RESULTS: Decreasing cortical GABAA conductance and increasing T-type calcium channel conductance converted spindle to spike and wave oscillations; smaller changes were required if both were changed in concert. In contrast, left shift of steady state voltage activation/inactivation did not lead to spike and wave oscillations, whereas right shift reduced network propensity for oscillations of any type...."
18.  Large cortex model with map-based neurons (Rulkov et al 2004)
We develop a new computationally efficient approach for the analysis of complex large-scale neurobiological networks. Its key element is the use of a new phenomenological model of a neuron capable of replicating important spike pattern characteristics and designed in the form of a system of difference equations (a map). ... Interconnected with synaptic currents these model neurons demonstrated responses very similar to those found with Hodgkin-Huxley models and in experiments. We illustrate the efficacy of this approach in simulations of one- and two-dimensional cortical network models consisting of regular spiking neurons and fast spiking interneurons to model sleep and activated states of the thalamocortical system. See paper for more.
19.  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. ..."
20.  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
21.  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."
22.  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. ..."
23.  Network model with neocortical architecture (Anderson et al 2007,2012; Azhar et al 2012)
Architecturally realistic neocortical model using seven classes of excitatory and inhibitory single compartment Hodgkin-Huxley cells. This is an addendum to ModelDB Accession # 98902, Studies of stimulus parameters for seizure disruption (Anderson et al. 2007). Wiring is adapted from the minicolumn hypothesis and incorporates visual and neocortical wiring data. Simulation demonstrates spontaneous bursting onset and cessation. This activity can be induced by random fluctuations in the surrounding background input.
24.  Parameter estimation for Hodgkin-Huxley based models of cortical neurons (Lepora et al. 2011)
Simulation and fitting of two-compartment (active soma, passive dendrite) for different classes of cortical neurons. The fitting technique indirectly matches neuronal currents derived from somatic membrane potential data rather than fitting the voltage traces directly. The method uses an analytic solution for the somatic ion channel maximal conductances given approximate models of the channel kinetics, membrane dynamics and dendrite. This approach is tested on model-derived data for various cortical neurons.
25.  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.
26.  Prosthetic electrostimulation for information flow repair in a neocortical simulation (Kerr 2012)
This model is an extension of a model (<a href="http://senselab.med.yale.edu/ModelDB/ShowModel.asp?model=138379">138379</a>) recently published in Frontiers in Computational Neuroscience. This model consists of 4700 event-driven, rule-based neurons, wired according to anatomical data, and driven by both white-noise synaptic inputs and a sensory signal recorded from a rat thalamus. Its purpose is to explore the effects of cortical damage, along with the repair of this damage via a neuroprosthesis.
27.  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
28.  Rate model of a cortical RS-FS-LTS network (Hayut et al. 2011)
A rate model of cortical networks composed of RS, FS and LTS neurons. Synaptic depression is modelled according to the Tsodyks-Markram scheme.
29.  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. ..."
30.  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. ... "
31.  Simulations of oscillations in piriform cortex (Wilson & Bower 1992)
"1. A large-scale computer model of the piriform cortex was constructed on the basis of the known anatomic and physiological organization of this region. 2. The oscillatory field potential and electroencephalographic (EEG) activity generated by the model was compared with actual physiological results. The model was able to produce patterns of activity similar to those recorded physiologically in response to both weak and strong electrical shocks to the afferent input. The model also generated activity patterns similar to EEGs recorded in behaving animals. 3. ..."
32.  Studies of stimulus parameters for seizure disruption using NN simulations (Anderson et al. 2007)
Architecturally realistic neocortical model using seven classes of excitatory and inhibitory single compartment Hodgkin-Huxley cells. Wiring is adapted to minicolumn hypothesis and incorporates visual and neocortical data. Simulation demonstrates spontaneous bursting onset and cessation, and activity can be altered with external electric field.
33.  Synaptic information transfer in computer models of neocortical columns (Neymotin et al. 2010)
"... We sought to measure how the activity of the network alters information flow from inputs to output patterns. Information handling by the network reflected the degree of internal connectivity. ... With greater connectivity strength, the recurrent network translated activity and information due to contribution of activity from intrinsic network dynamics. ... At still higher internal synaptic strength, the network corrupted the external information, producing a state where little external information came through. The association of increased information retrieved from the network with increased gamma power supports the notion of gamma oscillations playing a role in information processing."
34.  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.
35.  The origin of different spike and wave-like events (Hall et al 2017)
Acute In vitro models have revealed a great deal of information about mechanisms underlying many types of epileptiform activity. However, few examples exist that shed light on spike and wave (SpW) patterns of pathological activity. SpW are seen in many epilepsy syndromes, both generalised and focal, and manifest across the entire age spectrum. They are heterogeneous in terms of their severity, symptom burden and apparent anatomical origin (thalamic, neocortical or both), but any relationship between this heterogeneity and underlying pathology remains elusive. Here we demonstrate that physiological delta frequency rhythms act as an effective substrate to permit modelling of SpW of cortical origin and may help to address this issue. ..."
36.  Unbalanced peptidergic inhibition in superficial cortex underlies seizure activity (Hall et al 2015)
" ...Loss of tonic neuromodulatory excitation, mediated by nicotinic acetylcholine or serotonin (5HT3A) receptors, of 5HT3-immunopositive interneurons caused an increase in amplitude and slowing of the delta rhythm until each period became the "wave" component of the spike and wave discharge. As with the normal delta rhythm, the wave of a spike and wave discharge originated in cortical layer 5. In contrast, the "spike" component of the spike and wave discharge originated from a relative failure of fast inhibition in layers 2/3-switching pyramidal cell action potential outputs from single, sparse spiking during delta rhythms to brief, intense burst spiking, phase-locked to the field spike. The mechanisms underlying this loss of superficial layer fast inhibition, and a concomitant increase in slow inhibition, appeared to be precipitated by a loss of neuropeptide Y (NPY)-mediated local circuit inhibition and a subsequent increase in vasoactive intestinal peptide (VIP)-mediated disinhibition. Blockade of NPY Y1 receptors was sufficient to generate spike and wave discharges, whereas blockade of VIP receptors almost completely abolished this form of epileptiform activity. These data suggest that aberrant, activity-dependent neuropeptide corelease can have catastrophic effects on neocortical dynamics."
37.  Wang-Buzsaki Interneuron (Talathi et al., 2010)
The submitted code provides the relevant C++ files, matlabfiles and the data files essential to reproduce the figures in the JCNS paper titled Control of neural synchrony using channelrhodopsin-2: A computational study.

Re-display model names without descriptions