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

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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.
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;
Gap Junctions: Gap junctions;
Receptor(s): D1; D2; GabaA; AMPA; NMDA; Dopaminergic Receptor;
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]; Wood, Ric [ric.wood at];
Search NeuronDB for information about:  D1; D2; GabaA; AMPA; NMDA; Dopaminergic Receptor; Dopamine; Gaba; Glutamate;
function [gamma,gammaG] = clustind(CIJ);

% input:  CIJ    = connection/adjacency matrix
% output: gamma  = cluster index for each vertex
%         gammaG = cluster index for entire graph

% Compute cluster index.
% Watts/Strogatz use un-directed graphs, here we use directed graphs.
% Find the immediate neighbors of a given vertex (both in and out), 
% then determine how many connections exist between them out 
% of all possible.  Note: immediate neighbors are all those vertices that
% either send or receive an edge from the target vertex.
% Written by Olaf Sporns, Indiana University, 2002
% Bug fixed (handling orphan neigbors, previously gamma = NaN) - 2004
% - Thanks Constantin

N = size(CIJ,1);

gamma = [];
% loop over all vertices
for v=1:N
   [nb] = find(CIJ(v,:) + CIJ(:,v)');
   if (~isempty(nb))
      gamma = [gamma sum(sum(CIJ(nb,nb)))./(length(nb)^2-length(nb))];

% handle nodes that are connected to only a single other node
indices = find(isnan(gamma));
gamma(indices) = 0;

gammaG = mean(gamma);