Models that contain the Model Type : Neural mass

Re-display model names without descriptions
    Models   Description
1.  A bistable model of Spike-Wave seizure and background activity (Taylor et al. 2014)
This is a four-variable model (in the Amari formalism) of bistable Spike-Wave seizure dynamics and background activity (fixed point). The published code is the deterministic version of the model in the related publication. This model can be used to investigate seizure abatement using stimulation.
2.  A cortical sheet mesoscopic model for investigating focal seizure onset dynamics (Wang et al. 2014)
The model uses realistically coupled, discretised, Wilson-Cowan units to describe the spatio-temporal activity of a cortical sheet. This model has been used the investigate the dynamic onset mechanisms of focal seizures.
3.  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.
4.  A neural mass model for critical assessment of brain connectivity (Ursino et al 2020)
We use a neural mass model of interconnected regions of interest to simulate reliable neuroelectrical signals in the cortex. In particular, signals simulating mean field potentials were generated assuming two, three or four ROIs, connected via excitatory or by-synaptic inhibitory links. Then we investigated whether bivariate Transfer Entropy (TE) can be used to detect a statistically significant connection from data (as in binary 0/1 networks), and even if connection strength can be quantified (i.e., the occurrence of a linear relationship between TE and connection strength). Results suggest that TE can reliably estimate the strength of connectivity if neural populations work in their linear regions. However, nonlinear phenomena dramatically affect the assessment of connectivity, since they may significantly reduce TE estimation. Software included here allows the simulation of neural mass models with a variable number of ROIs and connections, the estimation of TE using the free package Trentool, and the realization of figures to compare true connectivity with estimated values.
5.  A neural mass model of cross frequency coupling (Chehelcheraghi et al 2017)
"Electrophysiological signals of cortical activity show a range of possible frequency and amplitude modulations, both within and across regions, collectively known as cross-frequency coupling. To investigate whether these modulations could be considered as manifestations of the same underlying mechanism, we developed a neural mass model. The model provides five out of the theoretically proposed six different coupling types. ..."
6.  A spatially extended model for macroscopic spike-wave discharges (Taylor and Baier 2011)
A spatially extended neural field model for generating spike-wave based on the Amari (1977) model implemented in MATLAB.
7.  Adaptive dual control of deep brain stimulation in Parkinsons disease simulations (Grado et al 2018)
8.  Basal ganglia-corticothalamic (BGCT) network (Chen et al., 2014)
We developed a biophysical model of the basal ganglia-corticothalamic network in this work. "... We demonstrate that the typical absence seizure activities can be controlled and modulated by the direct GABAergic projections from the substantia nigra pars reticulata (SNr) to either the thalamic reticular nucleus (TRN) or the specific relay nuclei (SRN) of thalamus, through different biophysical mechanisms. ... results highlight the bidirectional functional roles of basal ganglia in controlling and modulating absence seizures, and might provide novel insights into the therapeutic treatments of this brain disorder."
9.  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.
10.  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.
11.  E-I-E direction-selective motion discrimination visual cortex traveling waves (Heitmann et al 2020)
The direction-selective responses of neurons in visual cortex cannot be separated into independent spatial and temporal processes. Contemporary theories of how neurons compute non-separable responses typically rely on finely tuned transmission delays. However the existence of such delays is controversial. We propose an alternative neural mechanism for computing non-separable responses that relies on the predisposition of the cortical tissue to spontaneously generate traveling waves of neural activity. We propose that these endogenous waves resonate with the visual stimulus to elicit direction-selective neural responses without resort to time delays.
12.  Effect of cortical D1 receptor sensitivity on working memory maintenance (Reneaux & Gupta 2018)
Alterations in cortical D1 receptor density and reactivity of dopamine-binding sites, collectively termed as D1 receptor-sensitivity in the present study, have been experimentally shown to affect the working memory maintenance during delay-period. However, computational models addressing the effect of D1 receptor-sensitivity are lacking. A quantitative neural mass model of the prefronto-mesoprefrontal system has been proposed to take into account the effect of variation in cortical D1 receptor-sensitivity on working memory maintenance during delay. The model computes the delay-associated equilibrium states/operational points of the system for different values of D1 receptor-sensitivity through the nullcline and bifurcation analysis. Further, to access the robustness of the working memory maintenance during delay in the presence of alteration in D1 receptor-sensitivity, numerical simulations of the stochastic formulation of the model are performed to obtain the global potential landscape of the dynamics.
13.  Emergence of Connectivity Motifs in Networks of Model Neurons (Vasilaki, Giugliano 2014)
Recent evidence suggests that short-term dynamics of excitatory synaptic transmission is correlated to stereotypical connectivity motifs. We show that these connectivity motifs emerge in networks of model neurons, from the interactions between short-term synaptic dynamics (SD) and long-term spike-timing dependent plasticity (STDP).
14.  Entrainment and divisive inhibition in a neocortical neural mass model (Papasavvas et al 2020)
Neural mass model of a neocortical microcircuit featuring one excitatory and two inhibitory populations. The inhibitory populations represent the soma-targeting (parvalbumin) and dendrite-targeting (somatostatin) interneurons. The model uses the Wilson-Cowan formalism and differentiates between the two inhibitory populations by the way they modulate the input-output function of the excitatory population (subtractive vs divisive inhibition, based on Wilson et al., Nature, 7411, 488, 343-348, 2012). The connectivity patterns between the populations follow the patterns reported in the primary visual cortex (Pfeffer et al., Nat Neurosci 16, 1068–1076, 2013). The model is used here to investigate the role of divisive inhibition during the entrainment of the microcircuit.
15.  Human seizures couple across spatial scales through travelling wave dynamics (Martinet et al 2017)
" ... We show that during seizure large-scale neural populations spanning centimetres of cortex coordinate with small neural groups spanning cortical columns, and provide evidence that rapidly propagating waves of activity underlie this increased inter-scale coupling. We develop a corresponding computational model to propose specific mechanisms—namely, the effects of an increased extracellular potassium concentration diffusing in space—that support the observed spatiotemporal dynamics. Understanding the multi-scale, spatiotemporal dynamics of human seizures—and connecting these dynamics to specific biological mechanisms—promises new insights to treat this devastating disease.
16.  Hyperconnectivity, slow synapses in PFC mental retardation and autism model (Testa-Silva et al 2011)
The subdirectory 'matlab' contains MATLAB scripts (The Mathworks, USA) that can be used to reproduce the panels of Figures 4-5. This directory contains files to reproduce sample computer simulations presented in the 2011 paper authored by Meredith, R., Testa-Silva, G., Loebel, A., Giugliano, M., de Kock, C.; Mansvelder, H. "Hyperconnectivity and slow synapses in prefrontal cortex of a model for mental retardation and autism". ABSTRACT "... We propose that these findings are tightly linked: using a network model, we show that slower synapses are essential to counterbalance hyperconnectivity in order to maintain a dynamic range of excitatory activity. However, the slow synaptic time constants induce decreased responsiveness to low frequency stimulation, which may explain deficits in integration and information processing in attentional neuronal networks in neurodevelopmental disorders."
17.  Large-scale laminar model of macaque cortex (Mejias et al 2016)
This code reproduces the large-scale cortical model with laminar structure presented in Mejias et al., Science Advances 2016. The model includes different scales (intra-laminar, inter-laminar, inter-areal and large-scale) across macaque neocortex and reproduces experimentally observed dynamics of gamma and alpha/beta oscillations across these scales. It makes use of real anatomical data from the macaque cortex. Some parts of the code require external packages or data (see readme file for details).
18.  LGNcircuit: Minimal LGN network model of temporal processing of visual input (Norheim et al. 2012)
The responses of relay cells in the lateral geniculate nucleus (LGN) are shaped by their diverse set of impinging inputs: feedforward synaptic inputs stemming from retina, and feedback inputs stemming from the visual cortex and the thalamic reticular nucleus. This MATLAB model, with an easy-to-use graphical user interface (GUI), explores possible roles of these feedforward and feedback inputs in shaping the temporal part of the receptive fields of LGN relay cells with, so called, ON symmetry. A minimal mechanistic firing-rate model tailored to elucidate salient feedforward and feedback effects is considered including, in particular, feedforward excitation and inhibition (via interneurons) from retinal ON cells and excitatory and inhibitory (via thalamic reticular nucleus cells and interneurons) feedback from cortical ON and OFF cells. Various types of visual stimuli can be explored: flashing spots, impulses, sinusoidal gratings.
19.  Macroscopic model of epilepsy (Fietkiewicz & Loparo 2016)
Simulates epileptiform EEG. Original model used for Figure 2 in Fietkiewicz and Loparo 2016. The MATLAB program uses Euler integration to create the basic plot in Figure 2. The model is based on the original model specified in Wendling F, Bartolomei F, Bellanger JJ, Chauvel P. Epileptic fast activity can be explained by a model of impaired GABAergic dendritic inhibition. Eur J Neurosci, 2002;15(9):1499-1508.
20.  Mean-field models of neural populations under electrical stimulation (Cakan & Obermayer 2020)
Weak electrical inputs to the brain in vivo using transcranial electrical stimulation or in isolated cortex in vitro can affect the dynamics of the underlying neural populations. However, it is poorly understood what the exact mechanisms are that modulate the activity of neural populations as a whole and why the responses are so diverse in stimulation experiments. Despite this, electrical stimulation techniques are being developed for the treatment of neurological diseases in humans. To better understand these interactions, it is often necessary to simulate and analyze very large networks of neurons, which can be computationally demanding. In this theoretical paper, we present a reduced model of coupled neural populations that represents a piece of cortical tissue. This efficient model retains the dynamical properties of the large network of neurons it is based on while being several orders of magnitude faster to simulate. Due to the biophysical properties of the neuron model, an electric field can be coupled to the population. We show that weak electric fields often used in stimulation experiments can lead to entrainment of neural oscillations on the population level, and argue that the responses critically depend on the dynamical state of the neural system.
21.  Mechanisms underlying different onset patterns of focal seizures (Wang Y et al 2017)
"Focal seizures are episodes of pathological brain activity that appear to arise from a localised area of the brain. The onset patterns of focal seizure activity have been studied intensively, and they have largely been distinguished into two types { low amplitude fast oscillations (LAF), or high amplitude spikes (HAS). Here we explore whether these two patterns arise from fundamentally different mechanisms. Here, we use a previously established computational model of neocortical tissue, and validate it as an adequate model using clinical recordings of focal seizures. We then reproduce the two onset patterns in their most defining properties and investigate the possible mechanisms underlying the different focal seizure onset patterns in the model. ..."
22.  Modelling enteric neuron populations and muscle fed-state motor patterns (Chambers et al. 2011)
"After a meal, the gastrointestinal tract exhibits a set of behaviours known as the fed state. ... Segmentation manifests as rhythmic local constrictions that do not propagate along the intestine. ... We investigated the enteric circuits that regulate segmentation focusing on a central feature of the ENS: a recurrent excitatory network of intrinsic sensory neurons (ISNs) which are characterized by prolonged after-hyperpolarizing potentials (AHPs) following their action potentials. ..."
23.  Network model of movement disorders (Yousif et al 2020)
This is a Wilson-Cowan model of the basal ganglia thalamocortical cerebellar network that demonstrates healthy gamma band oscillations, Parkinsonian oscillations in the beta band and oscillations in the tremor frequency range arising from the dynamics of the network.
24.  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)
25.  Neural Mass Model for relationship between Brain Rhythms + Functional Connectivity (Ricci et al '21)
The Neural Mass Model (NMM) generates biologically reliable mean field potentials of four interconnected regions of interest (ROIs) of the cortex, each simulating a different brain rhythm (in theta, alpha, beta and gamma ranges). These neuroelectrical signals originate from the assumption that ROIs influence each other via of excitatory or by-synaptic inhibitory connections. Besides receiving long-range synapses from other ROIs, each one receives an external input and superimposed Gaussian white noise. We used the NMM to simulate different connectivity networks of four ROIs, by varying both the synaptic strengths and the inputs. The purpose of this study is to investigate how the transmission of brain rhythms behaves under linear and nonlinear conditions. To this aim, we investigated the performance of eight Functional Connectivity (FC) estimators (Correlation, Delayed Correlation, Coherence, Lagged Coherence, Temporal Granger Causality, Spectral Granger Causality, Phase Synchronization and Transfer Entropy) in detecting the connectivity network changes. Results suggest that when a ROI works in the linear region, its capacity to transmit its rhythm increases, while when it saturates, the oscillatory activity becomes strongly affected by other ROIs. Software included here allows the simulation of mean field potentials of four interconnected ROIs, their visualization, both in time and frequency domains, and the estimation of the related FC with eight different methods (for Transfer Entropy the Trentool package is needed).
26.  Neural mass model of spindle generation in the isolated thalamus (Schellenberger Costa et al. 2016)
The model generates different oscillatory patterns in the thalamus, including delta and spindle band oscillations.
27.  Neural mass model of the neocortex under sleep regulation (Costa et al 2016)
This model generates typical human EEG patterns of sleep stages N2/N3 as well as wakefulness and REM. It further contains a sleep regulatory component, that lets the model transition between those stages independently
28.  Neural mass model of the sleeping cortex (Weigenand et al 2014)
Generates typical EEG data of sleeping Humans for sleep stages N2/N3 as well as wakefulness
29.  Neural mass model of the sleeping thalamocortical system (Schellenberger Costa et al 2016)
This paper generates typical human EEG data of sleep stages N2/N3 as well as wakefulness and REM sleep.
30.  Neural-field model of generalized seizures (Zhao et al., 2015)
The mechanisms underlying generalized seizures are explored with neural field theory. A corticothalamic neural field model is used to explore changes leading to pathological seizure states. It is found that absence seizures arise from instabilities in the system and replicate experimental studies in numerous animal models and clinical studies.
31.  Nicotinic control of dopamine release in nucleus accumbens (Maex et al. 2014)
Minimal model of the VTA (ventral segmental area) representing two (GABA versus dopamine) neuron populations and two subtypes of nicotinic receptors (alpha4beta2 versus alpha7). The model is used to tell apart circuit from receptor mechanisms in the nicotinic control of dopamine release and its pharmacological manipulation.
32.  Synaptic damage underlies EEG abnormalities in postanoxic encephalopathy (Ruijter et al 2017)
"... In postanoxic coma, EEG patterns indicate the severity of encephalopathy and typically evolve in time. We aim to improve the understanding of pathophysiological mechanisms underlying these EEG abnormalities. ... We used a mean field model comprising excitatory and inhibitory neurons, local synaptic connections, and input from thalamic afferents. Anoxic damage is modeled as aggravated short-term synaptic depression, with gradual recovery over many hours. Additionally, excitatory neurotransmission is potentiated, scaling with the severity of anoxic encephalopathy. Simulations were compared with continuous EEG recordings of 155 comatose patients after cardiac arrest. ..."
33.  Thalamocortical model of spike and wave seizures (Suffczynski et al. 2004)
SIMULINK macroscopic model of transitions between normal (spindle) activity and spike and wave (SW) discharges in the thalamocortical network. The model exhibits bistability properties and stochastic fluctuations present in the network may flip the system between the two operational states. The predictions of the model were compared with real EEG data in rats and humans. A possibility to abort an ictal state by a single counter stimulus is suggested by the model.
34.  Universal feature of developing networks (Tabak et al 2010)
"Spontaneous episodic activity is a fundamental mode of operation of developing networks. Surprisingly, the duration of an episode of activity correlates with the length of the silent interval that precedes it, but not with the interval that follows. ... We thus developed simple models incorporating excitatory coupling between heterogeneous neurons and activity-dependent synaptic depression. These models robustly generated episodic activity with the correct correlation pattern. The correlation pattern resulted from episodes being triggered at random levels of recovery from depression while they terminated around the same level of depression. To explain this fundamental difference between episode onset and termination, we used a mean field model, where only average activity and average level of recovery from synaptic depression are considered. ... This work further shows that networks with widely different architectures, different cell types, and different functions all operate according to the same general mechanism early in their development."

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