Circuits that contain the Model Concept : Pathophysiology

(The physiology (specific functions) associated with a condition in which the normal functioning of an animal is impaired.)
Re-display model names without descriptions
    Models   Description
1. A basal ganglia model of aberrant learning (Ursino et al. 2018)
A comprehensive, biologically inspired neurocomputational model of action selection in the Basal Ganglia allows simulation of dopamine induced aberrant learning in Parkinsonian subjects. In particular, the model simulates the Alternate Finger Tapping motor task as an indicator of bradykinesia.
2. A dynamical model of the basal ganglia (Leblois et al 2006)
We propose a new model for the function and dysfunction of the basal ganglia (BG). The basal ganglia are a set of cerebral structures involved in motor control which dysfunction causes high-incidence pathologies such as Parkinson's disease (PD). Their precise motor functions remain unknown. The classical model of the BG that allowed for the discovery of new treatments for PD seems today outdated in several respects. Based on experimental observations, our model proposes a simple dynamical framework for the understanding of how BG may select motor programs to be executed. Moreover, we explain how this ability is lost and how tremor-related oscillations in neuronal activity may emerge in PD.
3. A model for focal seizure onset, propagation, evolution, and progression (Liou et al 2020)
We developed a neural network model that can account for major elements common to human focal seizures. These include the tonic-clonic transition, slow advance of clinical semiology and corresponding seizure territory expansion, widespread EEG synchronization, and slowing of the ictal rhythm as the seizure approaches termination. These were reproduced by incorporating usage-dependent exhaustion of inhibition in an adaptive neural network that receives global feedback inhibition in addition to local recurrent projections. Our model proposes mechanisms that may underline common EEG seizure onset patterns and status epilepticus, and postulates a role for synaptic plasticity in the emergence of epileptic foci. Complex patterns of seizure activity and bi- stable seizure end-points arise when stochastic noise is included. With the rapid advancement of clinical and experimental tools, we believe that this model can provide a roadmap and potentially an in silico testbed for future explorations of seizure mechanisms and clinical therapies.
4. 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.
5. A Neural mass computational model of the Thalamocorticothalamic circuitry (Bhattacharya et al. 2011)
The model presented here is a bio-physically plausible version of a simple thalamo-cortical neural mass computational model proposed by Lopes da Silva in 1974 to simulate brain EEG activity within the alpha band (8-13 Hz). The thalamic and cortical circuitry are presented as separate modules in this model with cell populations as in biology. The connectivity between cell populations are as reported by Sherman, S. in Scholarpedia, 2006. The values of the synaptic connectivity parameters are as reported by Van Horn et al, 2000. In our paper (doi:10.1016/j.neunet.2011.02.009), we study the model behaviour while varying the values of the synaptic connectivity parameters (Cyyy) in the model about their respective 'basal' (intial) values.
6. A neural model of Parkinson`s disease (Cutsuridis and Perantonis 2006, Cutsuridis 2006, 2007)
"A neural model of neuromodulatory (dopamine) control of arm movements in Parkinson’s disease (PD) bradykinesia was recently introduced [1, 2]. The model is multi-modular consisting of a basal ganglia module capable of selecting the most appropriate motor command in a given context, a cortical module for coordinating and executing the final motor commands, and a spino-musculo-skeletal module for guiding the arm to its final target and providing proprioceptive (feedback) input of the current state of the muscle and arm to higher cortical and lower spinal centers. ... The new (extended) model [3] predicted that the reduced reciprocal disynaptic Ia inhibition in the DA depleted case doesn’t lead to the co-contraction of antagonist motor units." See below readme and papers for more and details.
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. 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.
9. Activity patterns in a subthalamopallidal network of the basal ganglia model (Terman et al 2002)
"Based on recent experimental data, we have developed a conductance-based computational network model of the subthalamic nucleus and the external segment of the globus pallidus in the indirect pathway of the basal ganglia. Computer simulations and analysis of this model illuminate the roles of the coupling architecture of the network, and associated synaptic conductances, in modulating the activity patterns displayed by this network. Depending on the relationships of these coupling parameters, the network can support three general classes of sustained firing patterns: clustering, propagating waves, and repetitive spiking that may show little regularity or correlation. ...". Terman's XPP code and a partial implementation by Taylor Malone in NEURON and python are included.
10. Basal Ganglia and Levodopa Pharmacodynamics model for parameter estimation in PD (Ursino et al 2020)
Parkinson disease (PD) is characterized by a clear beneficial motor response to levodopa (LD) treatment. However, with disease progression and longer LD exposure, drug-related motor fluctuations usually occur. Recognition of the individual relationship between LD concentration and its effect may be difficult, due to the complexity and variability of the mechanisms involved. This work proposes an innovative procedure for the automatic estimation of LD pharmacokinetics and pharmacodynamics parameters, by a biologically-inspired mathematical model. An original issue, compared with previous similar studies, is that the model comprises not only a compartmental description of LD pharmacokinetics in plasma and its effect on the striatal neurons, but also a neurocomputational model of basal ganglia action selection. Parameter estimation was achieved on 26 patients (13 with stable and 13 with fluctuating LD response) to mimic plasma LD concentration and alternate finger tapping frequency along four hours after LD administration, automatically minimizing a cost function of the difference between simulated and clinical data points. Results show that individual data can be satisfactorily simulated in all patients and that significant differences exist in the estimated parameters between the two groups. Specifically, the drug removal rate from the effect compartment, and the Hill coefficient of the concentration-effect relationship were significantly higher in the fluctuating than in the stable group. The model, with individualized parameters, may be used to reach a deeper comprehension of the PD mechanisms, mimic the effect of medication, and, based on the predicted neural responses, plan the correct management and design innovative therapeutic procedures.
11. 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.
12. Basal ganglia-thalamic network model for deep brain stimulation (So et al. 2012)
This is a model of the basal ganglia-thalamic network, modified from the Rubin and Terman model (High frequency stimulation of the Subthalamic Nucleus, Rubin and Terman 2004). We subsequently used this model to investigate the effectiveness of STN and GPi DBS as well as lesion when various proportions of local cells and fibers of passage were activated or silenced. The BG network exhibited characteristics consistent with published experimental data, both on the level of single cells and on the network level. Perhaps most notably, and in contrast to the original RT model, the changes in the thalamic error index with changes in the DBS frequency matched well the changes in clinical symptoms with changes in DBS frequency.
13. Basal ganglia-thalamocortical loop model of action selection (Humphries and Gurney 2002)
We embed our basal ganglia model into a wider circuit containing the motor thalamocortical loop and thalamic reticular nucleus (TRN). Simulation of this extended model showed that the additions gave five main results which are desirable in a selection/switching mechanism. First, low salience actions (i.e. those with low urgency) could be selected. Second, the range of salience values over which actions could be switched between was increased. Third, the contrast between the selected and non-selected actions was enhanced via improved differentiation of outputs from the BG. Fourth, transient increases in the salience of a non-selected action were prevented from interrupting the ongoing action, unless the transient was of sufficient magnitude. Finally, the selection of the ongoing action persisted when a new closely matched salience action became active. The first result was facilitated by the thalamocortical loop; the rest were dependent on the presence of the TRN. Thus, we conclude that the results are consistent with these structures having clearly defined functions in action selection.
14. 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.
15. CA1 pyramidal cell: reconstructed axonal arbor and failures at weak gap junctions (Vladimirov 2011)
Model of pyramidal CA1 cells connected by gap junctions in their axons. Cell geometry is based on anatomical reconstruction of rat CA1 cell (NeuroMorpho.Org ID: NMO_00927) with long axonal arbor. Model init_2cells.hoc shows failures of second spike propagation in a spike doublet, depending on conductance of an axonal gap junction. Model init_ring.hoc shows that spike failure result in reentrant oscillations of a spike in a loop of axons connected by gap junctions, where one gap junction is weak. The paper shows that in random networks of axons connected by gap junctions, oscillations are driven by single pacemaker loop of axons. The shortest loop, around which a spike can travel, is the most likely pacemaker. This principle allows us to predict the frequency of oscillations from network connectivity and visa versa. We propose that this type of oscillations corresponds to so-called fast ripples in epileptic hippocampus.
16. CA3 Network Model of Epileptic Activity (Sanjay et. al, 2015)
This computational study investigates how a CA3 neuronal network consisting of pyramidal cells, basket cells and OLM interneurons becomes epileptic when dendritic inhibition to pyramidal cells is impaired due to the dysfunction of OLM interneurons. After standardizing the baseline activity (theta-modulated gamma oscillations), systematic changes are made in the connectivities between the neurons, as a result of step-wise impairment of dendritic inhibition.
17. Changes of ionic concentrations during seizure transitions (Gentiletti et al. 2016)
"... In order to investigate the respective roles of synaptic interactions and nonsynaptic mechanisms in seizure transitions, we developed a computational model of hippocampal cells, involving the extracellular space, realistic dynamics of Na+, K+, Ca2+ and Cl - ions, glial uptake and extracellular diffusion mechanisms. We show that the network behavior with fixed ionic concentrations may be quite different from the neurons’ behavior when more detailed modeling of ionic dynamics is included. In particular, we show that in the extended model strong discharge of inhibitory interneurons may result in long lasting accumulation of extracellular K+, which sustains the depolarization of the principal cells and causes their pathological discharges. ..."
18. Coding explains development of binocular vision and its failure in Amblyopia (Eckmann et al 2020)
This is the MATLAB code for the Active Efficient Coding model introduced in Eckmann et al 2020. It simulates an agent that self-calibrates vergence and accommodation eye movements in a simple visual environment. All algorithms are explained in detail in the main manuscript and the supplementary material of the paper.
19. Cognitive and motor cortico-basal ganglia interactions during decision making (Guthrie et al 2013)
This is a re-implementation of Guthrie et al 2013 by Topalidou and Rougier 2015. The original study investigated how multiple level action selection could be performed by the basal ganglia.
20. 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."
21. 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.
22. 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
23. 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.
24. Contribution of ATP-sensitive potassium channels in the neuronal network (Huang et al. 2009)
Epileptic seizures in diabetic hyperglycemia (DH) are not uncommon. This study aimed to determine the acute behavioral, pathological, and electrophysiological effects of status epilepticus (SE) on diabetic animals. ... We also used a simulation model to evaluate intracellular adenosine triphosphate (ATP) and neuroexcitability. ... In the simulation, increased intracellular ATP concentration promoted action potential firing. This finding that rats with DH had more brain damage after SE than rats without diabetes suggests the importance of intensively treating hyperglycemia and seizures in diabetic patients with epilepsy.
25. 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. ..."
26. 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.
27. Cortical oscillations and the basal ganglia (Fountas & Shanahan 2017)
"Although brain oscillations involving the basal ganglia (BG) have been the target of extensive research, the main focus lies disproportionally on oscillations generated within the BG circuit rather than other sources, such as cortical areas. We remedy this here by investigating the influence of various cortical frequency bands on the intrinsic effective connectivity of the BG, as well as the role of the latter in regulating cortical behaviour. To do this, we construct a detailed neural model of the complete BG circuit based on fine-tuned spiking neurons, with both electrical and chemical synapses as well as short-term plasticity between structures. As a measure of effective connectivity, we estimate information transfer between nuclei by means of transfer entropy. Our model successfully reproduces firing and oscillatory behaviour found in both the healthy and Parkinsonian BG. We found that, indeed, effective connectivity changes dramatically for different cortical frequency bands and phase offsets, which are able to modulate (or even block) information flow in the three major BG pathways. ..."
28. Cortico - Basal Ganglia Loop (Mulcahy et al 2020)
The model represents learning and reversal tasks and shows performance in control, Parkinsonian and Huntington disease conditions
29. 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."
30. Dentate gyrus (Morgan et al. 2007, 2008, Santhakumar et al. 2005, Dyhrfjeld-Johnsen et al. 2007)
This model was implemented by Rob Morgan in the Soltesz lab at UC Irvine. It is a scaleable model of the rat dentate gyrus including four cell types. This model runs in serial (on a single processor) and has been published at the size of 50,000 granule cells (with proportional numbers of the other cells).
31. Dentate gyrus network model (Santhakumar et al 2005)
Mossy cell loss and mossy fiber sprouting are two characteristic consequences of repeated seizures and head trauma. However, their precise contributions to the hyperexcitable state are not well understood. Because it is difficult, and frequently impossible, to independently examine using experimental techniques whether it is the loss of mossy cells or the sprouting of mossy fibers that leads to dentate hyperexcitability, we built a biophysically realistic and anatomically representative computational model of the dentate gyrus to examine this question. The 527-cell model, containing granule, mossy, basket, and hilar cells with axonal projections to the perforant-path termination zone, showed that even weak mossy fiber sprouting (10-15% of the strong sprouting observed in the pilocarpine model of epilepsy) resulted in the spread of seizure-like activity to the adjacent model hippocampal laminae after focal stimulation of the perforant path. See reference for more and details.
32. Dentate gyrus network model (Tejada et al 2014)
" ... Here we adapted an existing computational model of the dentate gyrus (J Neurophysiol 93: 437-453, 2005) by replacing the reduced granule cell models with morphologically detailed models coming from (3D) reconstructions of mature cells. ... Different fractions of the mature granule cell models were replaced by morphologically reconstructed models of newborn dentate granule cells from animals with PILO-induced Status Epilepticus, which have apical dendritic alterations and spine loss, and control animals, which do not have these alterations. This complex arrangement of cells and processes allowed us to study the combined effect of mossy fiber sprouting, altered apical dendritic tree and dendritic spine loss in newborn granule cells on the excitability of the dentate gyrus model. Our simulations suggest that alterations in the apical dendritic tree and dendritic spine loss in newborn granule cells have opposing effects on the excitability of the dentate gyrus after Status Epilepticus. Apical dendritic alterations potentiate the increase of excitability provoked by mossy fiber sprouting while spine loss curtails this increase. "
33. 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. ...
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. Electrodecrements in in vitro model of infantile spasms (Traub et al 2020)
The code is an extension of the thalamocortical model of Traub et al. (2005) J Neurophysiol. It is here applied to an in vitro model of the electrodecremental response seen in the EEG of children with infantile spasms (West syndrome)
37. 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. ... "
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. Epileptic seizure model with Morris-Lecar neurons (Beverlin and Netoff 2011)
Here we use phase-response curves (PRC) from Morris-Lecar (M-L) model neurons with synaptic depression and gradually decrease input current to cells within a network simulation. This method effectively decreases firing rates resulting in a shift to greater network synchrony illustrating a possible mechanism of the transition phenomenon. PRCs are measured from the M-L conductance based model cell with a range of input currents within the limit cycle. A large network of 3000 excitatory neurons is simulated with a network topology generated from second-order statistics which allows a range of population synchrony. The population synchrony of the oscillating cells is measured with the Kuramoto order parameter, which reveals a transition from tonic to clonic phase exhibited by our model network.
40. 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."
41. Failure of Deep Brain Stimulation in a basal ganglia neuronal network model (Dovzhenok et al. 2013)
"… Recently, a lot of interest has been devoted to desynchronizing delayed feedback deep brain stimulation (DBS). ... This study explores the action of delayed feedback stimulation on partially synchronized oscillatory dynamics, similar to what one observes experimentally in parkinsonian patients. …" Implemented by Andrey Dovzhenok, to whom questions should be addressed.
42. Functional consequences of cortical circuit abnormalities on gamma in schizophrenia (Spencer 2009)
"Schizophrenia is characterized by cortical circuit abnormalities, which might be reflected in gamma-frequency (30–100 Hz) oscillations in the electroencephalogram. Here we used a computational model of cortical circuitry to examine the effects that neural circuit abnormalities might have on gamma generation and network excitability. The model network consisted of 1000 leaky integrateand- fi re neurons with realistic connectivity patterns and proportions of neuron types [pyramidal cells (PCs), regular-spiking inhibitory interneurons, and fast-spiking interneurons (FSIs)]. ... The results of this study suggest that a multimodal approach, combining non-invasive neurophysiological and structural measures, might be able to distinguish between different neural circuit abnormalities in schizophrenia patients. ..."
43. Growth Rules for Repair of Asynch Irregular Networks after Peripheral Lesions (Sinha et al 2021)
A model of peripheral lesions and the resulting activity-dependent rewiring in a simplified balanced cortical network model that exhibits biologically realistic Asynchronous Irregular (AI) activity, used to derive activity dependent growth rules for different synaptic elements: dendritic and axonal.
44. High frequency stimulation of the Subthalamic Nucleus (Rubin and Terman 2004)
" ... Using a computational model, this paper considers the hypothesis that DBS works by replacing pathologically rhythmic basal ganglia output with tonic, high frequency firing. In our simulations of parkinsonian conditions, rhythmic inhibition from GPi to the thalamus compromises the ability of thalamocortical relay (TC) cells to respond to depolarizing inputs, such as sensorimotor signals. High frequency stimulation of STN regularizes GPi firing, and this restores TC responsiveness, despite the increased frequency and amplitude of GPi inhibition to thalamus that result. We provide a mathematical phase plane analysis of the mechanisms that determine TC relay capabilities in normal, parkinsonian, and DBS states in a reduced model. This analysis highlights the differences in deinactivation of the low-threshold calcium T -current that we observe in TC cells in these different conditions. ..."
45. 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).
46. Human Attentional Networks: A Connectionist Model (Wang and Fan 2007)
"... We describe a connectionist model of human attentional networks to explore the possible interplays among the networks from a computational perspective. This model is developed in the framework of leabra (local, error-driven, and associative, biologically realistic algorithm) and simultaneously involves these attentional networks connected in a biologically inspired way. ... We evaluate the model by simulating the empirical data collected on normal human subjects using the Attentional Network Test (ANT). The simulation results fit the experimental data well. In addition, we show that the same model, with a single parameter change that affects executive control, is able to simulate the empirical data collected from patients with schizophrenia. This model represents a plausible connectionist explanation for the functional structure and interaction of human attentional networks."
47. Huntington`s disease model (Gambazzi et al. 2010)
"Although previous studies of Huntington’s disease (HD) have addressed many potential mechanisms of striatal neuron dysfunction and death, it is also known based on clinical findings that cortical function is dramatically disrupted in HD. With respect to disease etiology, however, the specific molecular and neuronal circuit bases for the cortical effects of mutant huntingtin (htt) have remained largely unknown. In the present work we studied the relation between the molecular effects of mutant htt fragments in cortical cells and the corresponding behavior of cortical neuron microcircuits using a novel cellular model of HD. We observed that a transcript-selective diminution in activity-dependent BDNF expression preceded the onset of a synaptic connectivity deficit in ex vivo cortical networks, which manifested as decreased spontaneous collective burst-firing behavior measured by multi-electrode array substrates. Decreased BDNF expression was determined to be a significant contributor to network-level dysfunction, as shown by the ability of exogenous BDNF to ameliorate cortical microcircuit burst firing. The molecular determinants of the dysregulation of activity-dependent BDNF expression by mutant htt appear to be distinct from previously elucidated mechanisms, as they do not involve known NRSF/REST-regulated promoter sequences, but instead result from dysregulation of BDNF exon IV and VI transcription. These data elucidate a novel HD-related deficit in BDNF gene regulation as a plausible mechanism of cortical neuron hypoconnectivity and cortical function deficits in HD. Moreover, the novel model paradigm established here is well-suited to further mechanistic and drug screening research applications. A simple mathematical model is proposed to interpret the observations and to explore the impact of specific synaptic dysfunctions on network activity. Interestingly, the model predicts a decrease in synaptic connectivity to be an early effect of mutant huntingtin in cortical neurons, supporting the hypothesis of decreased, rather than increased, synchronized cortical firing in HD."
48. 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"
49. 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 ..."
50. Investigation of different targets in deep brain stimulation for Parkinson`s (Pirini et al. 2009)
"We investigated by a computational model of the basal ganglia the different network effects of deep brain stimulation (DBS) for Parkinson’s disease (PD) in different target sites in the subthalamic nucleus (STN), the globus pallidus pars interna (GPi), and the globus pallidus pars externa (GPe). A cellular-based model of the basal ganglia system (BGS), based on the model proposed by Rubin and Terman (J Comput Neurosci 16:211–235, 2004), was developed. ... Our results suggest that DBS in the STN could functionally restore the TC relay activity, while DBS in the GPe and in the GPi could functionally over-activate and inhibit it, respectively. Our results are consistent with the experimental and the clinical evidences on the network effects of DBS."
51. JitCon: Just in time connectivity for large spiking networks (Lytton et al. 2008)
This simulation is primarily an illustration and is not well optimized for actually running large networks. jitcon.mod contains a large amount of C level code, understanding of which requires some knowledge of Neuron internals
52. 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. ..."
53. MDD: the role of glutamate dysfunction on Cingulo-Frontal NN dynamics (Ramirez-Mahaluf et al 2017)
" ...Currently, no mechanistic framework describes how network dynamics, glutamate, and serotonin interact to explain MDD symptoms and treatments. Here, we built a biophysical computational model of 2 areas (vACC and dlPFC) that can switch between emotional and cognitive processing. (Major Depression Disease) MDD networks were simulated by slowing glutamate decay in vACC and demonstrated sustained vACC activation. ..."
54. Mechanisms of very fast oscillations in axon networks coupled by gap junctions (Munro, Borgers 2010)
Axons connected by gap junctions can produce very fast oscillations (VFOs, > 80 Hz) when stimulated randomly at a low rate. The models here explore the mechanisms of VFOs that can be seen in an axonal plexus, (Munro & Borgers, 2009): a large network model of an axonal plexus, small network models of axons connected by gap junctions, and an implementation of the model underlying figure 12 in Traub et al. (1999) . The large network model consists of 3,072 5-compartment axons connected in a random network. The 5-compartment axons are the 5 axonal compartments from the CA3 pyramidal cell model in Traub et al. (1994) with a fixed somatic voltage. The random network has the same parameters as the random network in Traub et al. (1999), and axons are stimulated randomly via a Poisson process with a rate of 2/s/axon. The small network models simulate waves propagating through small networks of axons connected by gap junctions to study how local connectivity affects the refractory period.
55. Minimal model of interictal and ictal discharges “Epileptor-2” (Chizhov et al 2018)
"Seizures occur in a recurrent manner with intermittent states of interictal and ictal discharges (IIDs and IDs). The transitions to and from IDs are determined by a set of processes, including synaptic interaction and ionic dynamics. Although mathematical models of separate types of epileptic discharges have been developed, modeling the transitions between states remains a challenge. A simple generic mathematical model of seizure dynamics (Epileptor) has recently been proposed by Jirsa et al. (2014); however, it is formulated in terms of abstract variables. In this paper, a minimal population-type model of IIDs and IDs is proposed that is as simple to use as the Epileptor, but the suggested model attributes physical meaning to the variables. The model is expressed in ordinary differential equations for extracellular potassium and intracellular sodium concentrations, membrane potential, and short-term synaptic depression variables. A quadratic integrate-and-fire model driven by the population input current is used to reproduce spike trains in a representative neuron. ..."
56. Model of arrhythmias in a cardiac cells network (Casaleggio et al. 2014)
" ... Here we explore the possible processes leading to the occasional onset and termination of the (usually) non-fatal arrhythmias widely observed in the heart. Using a computational model of a two-dimensional network of cardiac cells, we tested the hypothesis that an ischemia alters the properties of the gap junctions inside the ischemic area. ... In conclusion, our model strongly supports the hypothesis that non-fatal arrhythmias can develop from post-ischemic alteration of the electrical connectivity in a relatively small area of the cardiac cell network, and suggests experimentally testable predictions on their possible treatments."
57. Modeling brain dynamics in brain tumor patients using the Virtual Brain (Aerts et al 2018)
"Presurgical planning for brain tumor resection aims at delineating eloquent tissue in the vicinity of the lesion to spare during surgery. ... we simulated large-scale brain dynamics in 25 human brain tumor patients and 11 human control participants using The Virtual Brain, an open-source neuroinformatics platform. Local and global model parameters of the Reduced Wong–Wang model were individually optimized and compared between brain tumor patients and control subjects. In addition, the relationship between model parameters and structural network topology and cognitive performance was assessed. Results showed (1) significantly improved prediction accuracy of individual functional connectivity when using individually optimized model parameters; (2) local model parameters that can differentiate between regions directly affected by a tumor, regions distant from a tumor, and regions in a healthy brain; and (3) interesting associations between individually optimized model parameters and structural network topology and cognitive performance."
58. Modeling epileptic seizure induced by depolarization block (Kim & Dykamp 2017)
"The inhibitory restraint necessary to suppress aberrant activity can fail when inhibitory neurons cease to generate action potentials as they enter depolarization block. We investigate possible bifurcation structures that arise at the onset of seizure-like activity resulting from depolarization block in inhibitory neurons. Networks of conductance based excitatory and inhibitory neurons are simulated to characterize different types of transitions to the seizure state, and a mean field model is developed to verify the generality of the observed phenomena of excitatory-inhibitory dynamics. ..."
59. 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.
60. Multiscale modeling of epileptic seizures (Naze et al. 2015)
" ... In the context of epilepsy, the functional properties of the network at the source of a seizure are disrupted by a possibly large set of factors at the cellular and molecular levels. It is therefore needed to sacrifice some biological accuracy to model seizure dynamics in favor of macroscopic realizations. Here, we present a neuronal network model that convenes both neuronal and network representations with the goal to describe brain dynamics involved in the development of epilepsy. We compare our modeling results with animal in vivo recordings to validate our approach in the context of seizures. ..."
61. 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. ..."
62. 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)
63. Neocort. pyramidal cells subthreshold somatic voltage controls spike propagation (Munro Kopell 2012)
There is suggestive evidence that pyramidal cell axons in neocortex may be coupled by gap junctions into an ``axonal plexus" capable of generating Very Fast Oscillations (VFOs) with frequencies exceeding 80 Hz. It is not obvious, however, how a pyramidal cell in such a network could control its output when action potentials are free to propagate from the axons of other pyramidal cells into its own axon. We address this problem by means of simulations based on 3D reconstructions of pyramidal cells from rat somatosensory cortex. We show that somatic depolarization enables propagation via gap junctions into the initial segment and main axon, while somatic hyperpolarization disables it. We show further that somatic voltage cannot effectively control action potential propagation through gap junctions on minor collaterals; action potentials may therefore propagate freely from such collaterals regardless of somatic voltage. In previous work, VFOs are all but abolished during the hyperpolarization phase of slow-oscillations induced by anesthesia in vivo. This finding constrains the density of gap junctions on collaterals in our model and suggests that axonal sprouting due to cortical lesions may result in abnormally high gap junction density on collaterals, leading in turn to excessive VFO activity and hence to epilepsy via kindling.
64. Network model with dynamic ion concentrations (Ullah et al. 2009)
This is a network model composed of 100 excitatory and 100 inhibitory neurons with dynamic ion concentrations as described in "The Influence of Sodium and Potassium Dynamics on Excitability, Seizures, and the Stability of Persistent States: II. Network and Glia Dynamics (2009) Journal of Computational Neuroscience, 26:171-183".
65. 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.
66. Neural mass model based on single cell dynamics to model pathophysiology (Zandt et al 2014)
The model code as described in "A neural mass model based on single cell dynamics to model pathophysiology, Zandt et al. 2014, Journal of Computational Neuroscience" A Neural mass model (NMM) derived from single cell dynamics in a bottom up approach. Mean and standard deviation of the firing rates in the populations are calculated. The sigmoid is derived from the single cell FI-curve, allowing for easy implementation of pathological conditions. NMM is compared with a detailed spiking network model consisting of HH neurons. NMM code in Matlab. The network model is simulated using Norns (ModelDB # 154739)
67. Neuronal population models of intracerebral EEG (Wendling et al. 2005)
"... In this study, the authors relate electrophysiologic patterns typically observed during the transition from interictal to ictal activity in human mesial temporal lobe epilepsy (MTLE) to mechanisms (at a neuronal population level) involved in seizure generation through a computational model of EEG activity. Intracerebral EEG signals recorded from hippocampus in five patients with MTLE during four periods (during interictal activity, just before seizure onset, during seizure onset, and during ictal activity) were used to identify the three main parameters of a model of hippocampus EEG activity (related to excitation, slow dendritic inhibition and fast somatic inhibition). ... . Results demonstrated that the model generates very realistic signals for automatically identified parameters. They also showed that the transition from interictal to ictal activity cannot be simply explained by an increase in excitation and a decrease in inhibition but rather by time-varying ensemble interactions between pyramidal cells and local interneurons projecting to either their dendritic or perisomatic region (with slow and fast GABAA kinetics). Particularly, during preonset activity, an increasing dendritic GABAergic inhibition compensates a gradually increasing excitation up to a brutal drop at seizure onset when faster oscillations (beta and low gamma band, 15 to 40 Hz) are observed. ... These findings obtained from model identification in human temporal lobe epilepsy are in agreement with some results obtained experimentally, either on animal models of epilepsy or on the human epileptic tissue."
68. Normal ripples, abnormal ripples, and fast ripples in a hippocampal model (Fink et al. 2015)
"...We use a computational model of hippocampus to investigate possible network mechanisms underpinning normal ripples, pathological ripples, and fast ripples. Our results unify several prior findings regarding HFO mechanisms, and also make several new predictions regarding abnormal HFOs. We show that HFOs are generic, emergent phenomena whose characteristics reflect a wide range of connectivity and network input. Although produced by different mechanisms, both normal and abnormal HFOs generate similar ripple frequencies, underscoring that peak frequency is unable to distinguish the two. Abnormal ripples are generic phenomena that arise when input to pyramidal cells overcomes network inhibition, resulting in high-frequency, uncoordinated firing. In addition, fast ripples transiently and sporadically arise from the precise conditions that produce abnormal ripples. Lastly, we show that such abnormal conditions do not require any specific network structure to produce coherent HFOs, as even completely asynchronous activity is capable of producing abnormal ripples and fast ripples in this manner. These results provide a generic, network-based explanation for the link between pathological ripples and fast ripples, and a unifying description for the entire spectrum from normal ripples to pathological fast ripples."
69. 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.
70. 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.
71. Population-level model of the basal ganglia and action selection (Gurney et al 2001, 2004)
We proposed a new functional architecture for the basal ganglia (BG) based on the premise that these brain structures play a central role in behavioural action selection. The papers quantitatively describes the properties of the model using analysis and simulation. In the first paper, we show that the decomposition of the BG into selection and control pathways is supported in several ways. First, several elegant features are exposed--capacity scaling, enhanced selectivity and synergistic dopamine modulation--which might be expected to exist in a well designed action selection mechanism. Second, good matches between model GPe output and GPi and SNr output, and neurophysiological data, have been found. Third, the behaviour of the model as a signal selection mechanism has parallels with some kinds of action selection observed in animals under various levels of dopaminergic modulation. In the second paper, we extend the BG model to include new connections, and show that action selection is maintained. In addition, we provide quantitative measures for defining different forms of selection, and methods for assessing performance changes in computational neuroscience models.
72. Principles of Computational Modelling in Neuroscience (Book) (Sterratt et al. 2011)
"... This book provides a step-by-step account of how to model the neuron and neural circuitry to understand the nervous system at all levels, from ion channels to networks. Starting with a simple model of the neuron as an electrical circuit, gradually more details are added to include the effects of neuronal morphology, synapses, ion channels and intracellular signaling. The principle of abstraction is explained through chapters on simplifying models, and how simplified models can be used in networks. This theme is continued in a final chapter on modeling the development of the nervous system. Requiring an elementary background in neuroscience and some high school mathematics, this textbook is an ideal basis for a course on computational neuroscience."
73. 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.
74. 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.
75. 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).
76. SCN1A gain-of-function in early infantile encephalopathy (Berecki et al 2019)
"OBJECTIVE: To elucidate the biophysical basis underlying the distinct and severe clinical presentation in patients with the recurrent missense SCN1A variant, p.Thr226Met. Patients with this variant show a well-defined genotype-phenotype correlation and present with developmental and early infantile epileptic encephalopathy that is far more severe than typical SCN1A Dravet syndrome. METHODS: Whole cell patch clamp and dynamic action potential clamp were used to study T226M Nav 1.1 channels expressed in mammalian cells. Computational modeling was used to explore the neuronal scale mechanisms that account for altered action potential firing. RESULTS: T226M channels exhibited hyperpolarizing shifts of the activation and inactivation curves and enhanced fast inactivation. Dynamic action potential clamp hybrid simulation showed that model neurons containing T226M conductance displayed a left shift in rheobase relative to control. At current stimulation levels that produced repetitive action potential firing in control model neurons, depolarization block and cessation of action potential firing occurred in T226M model neurons. Fully computationally simulated neuron models recapitulated the findings from dynamic action potential clamp and showed that heterozygous T226M models were also more susceptible to depolarization block. ..."
77. Single compartment Dorsal Lateral Medium Spiny Neuron w/ NMDA and AMPA (Biddell and Johnson 2013)
A biophysical single compartment model of the dorsal lateral striatum medium spiny neuron is presented here. The model is an implementation then adaptation of a previously described model (Mahon et al. 2002). The model has been adapted to include NMDA and AMPA receptor models that have been fit to dorsal lateral striatal neurons. The receptor models allow for excitation by other neuron models.
78. Small world networks of Type I and Type II Excitable Neurons (Bogaard et al. 2009)
Implemented with NEURON 5.9, four model neurons with varying excitability properties affect the spatiotemporal patterning of small world networks of homogeneous and heterogeneous cell population.
79. 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.
80. 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.
81. 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.
82. 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.
83. 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.
84. 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.
85. Study of augmented Rubin and Terman 2004 deep brain stim. model in Parkinsons (Pascual et al. 2006)
" ... The model by Rubin and Terman [31] represents one of the most comprehensive and biologically plausible models of DBS published recently. We examined the validity of the model, replicated its simulations and tested its robustness. While our simulations partially reproduced the results presented by Rubin and Terman [31], several issues were raised including the high complexity of the model in its non simplified form, the lack of robustness of the model with respect to small perturbations, the nonrealistic representation of the thalamus and the absence of time delays. Computational models are indeed necessary, but they may not be sufficient in their current forms to explain the effect of chronic electrical stimulation on the activity of the basal ganglia (BG) network in PD."
86. 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.
87. Synchronicity of fast-spiking interneurons balances medium-spiny neurons (Damodaran et al. 2014)
This study investigates the role of feedforward and feedback inhibition in maintaining the balance between D1 and D2 MSNs of the striatum. The synchronized firing of FSIs are found to be critical in this mechanism and specifically the gap junction connections between FSIs.
88. 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."
89. Thalamic transformation of pallidal input (Hadipour-Niktarash 2006)
"In Parkinson’s disease, neurons of the internal segment of the globus pallidus (GPi) display the low-frequency tremor-related oscillations. These oscillatory activities are transmitted to the thalamic relay nuclei. Computer models of the interacting thalamocortical (TC) and thalamic reticular (RE) neurons were used to explore how the TC-RE network processes the low-frequency oscillations of the GPi neurons. ..."
90. Thalamo-cortical microcircuit (TCM) (AmirAli Farokhniaee and Madeleine M. Lowery 2021)
This is a model of exaggerated beta rhythm observed in the motor cortex, similar to animal and humans with Parkinson’s disease. It is obtained by manually changing the specific cortical, thalamic and thalamocortical synaptic connections, motivated by the previous studies in the field. More importantly and in addition, it serves as a thalamocortical network model of deep brain stimulation, a therapy used for Parkinson’s disease. We computationally stimulated the layer 5 pyramidal neurons of the cortex by direct injected currents to those pyramidal cells and observed well-known patterns in experimental studies, such as attenuation of the exaggerated beta rhythm, formation of excited and inhibited clusters of neurons in the motor cortex and the optimum value for the stimulation, both in amplitude and frequency domains, to obtain the most attenuated beta rhythm.
91. 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. ..."
92. Tonic-clonic transitions in a seizure simulation (Lytton and Omurtag 2007)
"... The authors have ... computationally manageable networks of moderate size consisting of 1,000 to 3,000 neurons with multiple intrinsic and synaptic properties. Experiments on these simulations demonstrated the presence of epileptiform behavior in the form of repetitive high-intensity population events (clonic behavior) or latch-up with near maximal activity (tonic behavior). ... Several simulations revealed the importance of random coincident inputs to shift a network from a low-activation to a high-activation epileptiform state. Finally, a simulated anticonvulsant acting on excitability tended to preferentially decrease tonic activity."
93. 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."

Re-display model names without descriptions