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Striatal GABAergic microcircuit, spatial scales of dynamics (Humphries et al, 2010)

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The main thrust of this paper was the development of the 3D anatomical network of the striatum's GABAergic microcircuit. We grew dendrite and axon models for the MSNs and FSIs and extracted probabilities for the presence of these neurites as a function of distance from the soma. From these, we found the probabilities of intersection between the neurites of two neurons given their inter-somatic distance, and used these to construct three-dimensional striatal networks. These networks were examined for their predictions for the distributions of the numbers and distances of connections for all the connections in the microcircuit. We then combined the neuron models from a previous model (Humphries et al, 2009; ModelDB ID: 128874) with the new anatomical model. We used this new complete striatal model to examine the impact of the anatomical network on the firing properties of the MSN and FSI populations, and to study the influence of all the inputs to one MSN within the network.
1 . Humphries MD, Wood R, Gurney K (2010) Reconstructing the three-dimensional GABAergic microcircuit of the striatum. PLoS Comput Biol 6:e1001011 [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: Striatum;
Cell Type(s): Neostriatum fast spiking interneuron;
Gap Junctions: Gap junctions;
Receptor(s): D1; D2; GabaA; AMPA; NMDA;
Transmitter(s): Dopamine; Gaba; Glutamate;
Simulation Environment: MATLAB;
Model Concept(s): Activity Patterns; Spatio-temporal Activity Patterns; Winner-take-all; 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; Dopamine; Gaba; Glutamate;
function out = Experiment_RandomInput(DA, fname)
if nargin == 0
    DA = 0.0;
    fname = {'RandomInput'};

% 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; 

% these will need re-scaling to account for removal of 1/tau_s if using RK2_B engine = ones(length(,1) .* 6.1; = ones(length(,1) .* 6.1; = ones(length(,1) .* 4.36; = ones(length(,1) .* (4.36 * 5); = ones(length(,1) .* (4.36 * 5); % same as FS-MSN input.... = ones(length(,1).* (150/5); % too strong?

% set the input parameters - overrides settings in StriatumNetworkParameters 
SIMPARAMS.sim.tfinal = 10000; % msec
% SIMPARAMS.input.CTX.r_MSSEG = ones(,1) .* 1.9; % Hz
% SIMPARAMS.input.CTX.N_MSSEG = int32(ones(,1) .* 250);
% SIMPARAMS.input.CTX.r_FSSEG = ones(,1) .* 1.9; % Hz
% SIMPARAMS.input.CTX.N_FSSEG = int32(ones(,1) .* 250);
% SIMPARAMS.input.CTX.alpha_FSSEG = ones(,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), '.')
figure(2); clf; plot(out.STfs(:,2), out.STfs(:,1), '.')
figure(3); clf; plot(out.RecordChan_MS(:,1:25))
figure(4); clf; plot(out.RecordChan_MS(:,26:end))

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