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]
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;
%%% script to assess basic firing stats of simulations, called by
%%% retained_graph_properties.m

% get spike data for neurons
% MSspks = out.STms; 
% MSspks(:,1) = MSspks(:,1)+1; % change from zero-base to 1-base index 
MSidxs = unique(MSspks(:,1));
Nmsidxs = numel(MSidxs);
Nms =;

% FSspks = out.STfs;
% FSspks(:,1) = FSspks(:,1)+1; % change from zero-base to 1-base index 
FSidxs = unique(FSspks(:,1));
Nfsidxs = numel(FSidxs);
Nfs =;

% simulation parameters
simT = SIMPARAMS.sim.tfinal; % in ms
T = simT * 1e-3;    % in seconds

% get stats for all MSNs
MSrate = zeros(Nms,1); MS_CVisi = zeros(Nms,1);
for j = 1:Nmsidxs
    currix = find(MSspks(:,1) == MSidxs(j));
    % basic rate
    MSrate(MSidxs(j)) = numel(currix) / T;
    % ISIs
    ts = MSspks(currix,2)*1e-3; % in seconds
    ISIs = diff(ts);
    MS_CVisi(MSidxs(j)) = std(ISIs)/mean(ISIs);

% get stats for all FSIs
FSrate = zeros(Nfs,1); FS_CVisi = zeros(Nfs,1);
for j = 1:Nfsidxs
    currix = find(FSspks(:,1) == FSidxs(j));
    % basic rate
    FSrate(FSidxs(j)) = numel(currix) / T;
    % ISIs
    ts = FSspks(currix,2)*1e-3; % in seconds
    ISIs = diff(ts);
    FS_CVisi(FSidxs(j)) = std(ISIs)/mean(ISIs);

%%%% plot empirical CDFs... 
%%% NOTE that stats structure returned by cdfplot has all info you need in
%%% it...
[hM,MSstats] = cdfplot(MSrate); hold on
[hF,FSstats] = cdfplot(FSrate); set(hF,'Color',[1 0 0])
phF = get(hF,'Parent'); set(phF,'XScale','log')
title('ECDF plots for firing rates')
xlabel('x=firing rate (spikes/s)')

[hMCV,MSCVstats] = cdfplot(MS_CVisi); hold on
[hFCV,FSCVstats] = cdfplot(FS_CVisi(~isnan(FS_CVisi))); set(hFCV,'Color',[1 0 0])
title('ECDF plots for ISI CV')
xlabel('x=ISI CV')

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