Striatal GABAergic microcircuit, dopamine-modulated cell assemblies (Humphries et al. 2009)

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Accession:128874
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.
Reference:
1 . Humphries MD, Wood R, Gurney K (2009) Dopamine-modulated dynamic cell assemblies generated by the GABAergic striatal microcircuit. Neural Netw 22:1174-88 [PubMed]
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Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network; Neuron or other electrically excitable cell;
Brain Region(s)/Organism:
Cell Type(s): Neostriatum fast spiking interneuron;
Channel(s):
Gap Junctions: Gap junctions;
Receptor(s): D1; D2; GabaA; AMPA; NMDA; Dopaminergic Receptor;
Gene(s):
Transmitter(s): Dopamine; Gaba; Glutamate;
Simulation Environment: MATLAB;
Model Concept(s): Activity Patterns; Temporal Pattern Generation; Synchronization; Spatio-temporal Activity Patterns; Parkinson's; Action Selection/Decision Making; Connectivity matrix;
Implementer(s): Humphries, Mark D [m.d.humphries at shef.ac.uk]; Wood, Ric [ric.wood at shef.ac.uk];
Search NeuronDB for information about:  D1; D2; GabaA; AMPA; NMDA; Dopaminergic Receptor; Dopamine; Gaba; Glutamate;
function P = expectedA(A)
% EXPECTEDA computes the expected number of connections between each vertex
%   P = EXPECTEDA(A) computes the matrix P of the expected number of
%   connections between each vertex, given the adjacency matrix A.
%
%   Notes: 
%   (1) the expected number of connection is computed based on the
%   assumption of the "null model": i.e. random connections between nodes
%   of the same degree sequence as A. Thus, for undirected graphs, Pij =
%   (ki*kj) / 2m. For directed graphs, we have to make the additional
%   assumption that the in-degree and out-degree distributions are
%   themselves not correlated - i.e. that we can place an edge between any
%   randomly selected pair of available out-nodes and in-nodes.
%
%   (2) In addition, the underlying "null model" allows multiple and
%   self-edges, and hence has values along the diagonal.
%
%   References: Newman, M. E. J. (2006) "Finding community structure in
%   networks using the eigenvectors of matrices". Phys Rev E, 74, 036104.
%
%   Mark Humphries 24/8/2006

[n c] = size(A);
inks = sum(A);
outks = sum(A');
m = sum(inks);  % number of edges

P = (outks' * inks) ./ m;