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 C = clusttriang(A)

% CLUSTTRIANG computes clustering coefficient based on number of triangles
%   C = CLUSTTRIANG(A) compute the clustering coefficient C of the
%   adjacency matrix A, based on the ratio (number of triangles)/(number of triples).
%   Notes:
%   (1)This is a subtly different definition than that of Watts & Strogatz (1998). 
%   In the present form, it does give the fraction of neighbouring nodes
%   that are also neighbours of each other. (Also note: for directed
%   graphs, this means *any* direction of connection, as along as all three
%   nodes are connected).
%   (2) This function uses the method of Keeling (1999) to compute C. 
%   Mark Humphries 22/8/2006

A2 = A * A;
A3 = A2 * A;
sumA2 = sum(sum(A2));

C = trace(A3) / (sumA2 - trace(A2));