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]
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 out = Experiment_RandomInput(DA, fname)
if nargin == 0
    DA = 0;
    fname = {'RandomInput'};
end

% set the model parameters
SIMPARAMS = StriatumNetworkParameters;

% name for the log file
SIMPARAMS.sim.logfname = [char(fname) '.log'];

% -------------------------------------------------------------------------
% set the DA level
SIMPARAMS.physiology.DA = DA; 

% set all the GABA the weights to 0
SIMPARAMS.net.Cctms_w = ones(length(SIMPARAMS.net.Cctms),1) .* 6.1;
SIMPARAMS.net.Cctfs_w = ones(length(SIMPARAMS.net.Cctfs),1) .* 6.1;
SIMPARAMS.net.Cmsms_w = ones(length(SIMPARAMS.net.Cmsms),1) .* 4.36;
SIMPARAMS.net.Cfsms_w = ones(length(SIMPARAMS.net.Cfsms),1) .* (4.36 * 5);
SIMPARAMS.net.Cfsfs_w = ones(length(SIMPARAMS.net.Cfsfs),1) .* (4.36 * 5);
SIMPARAMS.net.Cgapfs_w = ones(length(SIMPARAMS.net.Cgapfs_w),1).* (150/5); 

% set the input parameters
SIMPARAMS.sim.tfinal = 10000; % length of simulation in msec
SIMPARAMS.input.CTX.r_MSSEG = ones(SIMPARAMS.net.MS.N,1) .* 1.9; % Hz
SIMPARAMS.input.CTX.N_MSSEG = int32(ones(SIMPARAMS.net.MS.N,1) .* 250);
SIMPARAMS.input.CTX.r_FSSEG = ones(SIMPARAMS.net.FS.N,1) .* 1.9; % Hz
SIMPARAMS.input.CTX.N_FSSEG = int32(ones(SIMPARAMS.net.FS.N,1) .* 250);
SIMPARAMS.input.CTX.alpha_FSSEG = ones(SIMPARAMS.net.FS.N,1) .* 0.0;

save([char(fname) '_SIMPARAMS'],'SIMPARAMS');
% -------------------------------------------------------------------------
% Run the simulation
out = RunSimulation(SIMPARAMS);

% -------------------------------------------------------------------------
% Save the results to disc
save(char(fname), 'out');

% -------------------------------------------------------------------------
% plot the results
close all
figure(1); clf; plot(out.STms(:,2), out.STms(:,1), '.'); title('MSN raster plot')
figure(2); clf; plot(out.STfs(:,2), out.STfs(:,1), '.'); title('FSI raster plot')
figure(3); clf; plot(out.RecordChan_MS(:,1:25))
figure(4); clf; plot(out.RecordChan_MS(:,26:end))

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