| | Models | Description |
| 1. |
A model of working memory for encoding multiple items (Ursino et al, in press)
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We present an original neural network model, based on oscillating neural masses, to investigate mechanisms at the basis of working memory in different conditions. Simulations show that the trained network is able to desynchronize up to nine items without a fixed order using the gamma rhythm. Moreover, the network can replicate a sequence of items using a gamma rhythm nested inside a theta rhythm. The reduction in some parameters, mainly concerning the strength of GABAergic synapses, induce memory alterations which mimic neurological deficits. Finally, the network, isolated from the external environment simulates an“imagination phase”. |
| 2. |
A Neural mass computational model of the Thalamocorticothalamic circuitry (Bhattacharya et al. 2011)
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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. |
| 3. |
A neural mass model for critical assessment of brain connectivity (Ursino et al 2020)
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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. |
| 4. |
Biologically Constrained Basal Ganglia model (BCBG model) (Lienard, Girard 2014)
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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. |
| 5. |
Composite spiking network/neural field model of Parkinsons (Kerr et al 2013)
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This code implements a composite model of Parkinson's disease (PD). The
composite model consists of a leaky integrate-and-fire spiking neuronal
network model being driven by output from a neural field model (instead
of the more usual white noise drive). Three different sets of parameters
were used for the field model: one with basal ganglia parameters based
on data from healthy individuals, one based on data from individuals
with PD, and one purely thalamocortical model. The aim of this model is
to explore how the different dynamical patterns in each each of these
field models affects the activity in the network model. |
| 6. |
Distributed working memory in large-scale macaque brain model (Mejias and Wang, 2022)
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This code simulates working memory in a large-scale cortical network of the macaque brain. The model is constrained by anatomical data and provides a simple framework to explain the widespread activation of cortical areas during working memory. |
| 7. |
Emergence of Connectivity Motifs in Networks of Model Neurons (Vasilaki, Giugliano 2014)
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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). |
| 8. |
Hyperconnectivity, slow synapses in PFC mental retardation and autism model (Testa-Silva et al 2011)
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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." |
| 9. |
LGNcircuit: Minimal LGN network model of temporal processing of visual input (Norheim et al. 2012)
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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. |
| 10. |
Mean-field models of neural populations under electrical stimulation (Cakan & Obermayer 2020)
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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. |
| 11. |
Modelling enteric neuron populations and muscle fed-state motor patterns (Chambers et al. 2011)
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"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.
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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.
..." |
| 12. |
Motor Cortex Connectivity & Event Related Desynchronization Based on Neural Mass Models (Ursino 21)
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Knowledge of motor cortex connectivity is of great value in cognitive neuroscience, in order to provide a better understanding of motor organization and its alterations in pathological conditions. Traditional methods provide connectivity estimations which may vary depending on the task. This work aims to propose a new method for motor connectivity assessment based on the hypothesis of a task-independent connectivity network, assuming nonlinear behavior. The model considers six cortical regions of interest (ROIs) involved in hand movement. The dynamics of each region is simulated using a neural mass model, which reproduces the oscillatory activity through the interaction among four neural populations. Parameters of the model have been assigned to simulate both power spectral densities and coherences of a patient with left-hemisphere stroke during: resting condition, movement of the affected and movement of the unaffected hand. The presented model can simulate the three conditions using a single set of connectivity parameters, assuming that only inputs to the ROIs change from one condition to the other. The proposed procedure represents an innovative method to assess a brain circuit, which does not rely on a task-dependent connectivity network, and allows brain rhythms and desynchronization to be assessed on a quantitative basis.
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| 13. |
Neural mass model based on single cell dynamics to model pathophysiology (Zandt et al 2014)
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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) |
| 14. |
Neural Mass Model for relationship between Brain Rhythms + Functional Connectivity (Ricci et al '21)
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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). |
| 15. |
Universal feature of developing networks (Tabak et al 2010)
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"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." |
