Spiking neuron model of the basal ganglia (Humphries et al 2006)

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A spiking neuron model of the basal ganglia (BG) circuit (striatum, STN, GP, SNr). Includes: parallel anatomical channels; tonic dopamine; dopamine receptors in striatum, STN, and GP; burst-firing in STN; GABAa, AMPA, and NMDA currents; effects of synaptic location. Model demonstrates selection and switching of input signals. Replicates experimental data on changes in slow-wave (<1 Hz) and gamma-band oscillations within BG nuclei following lesions and pharmacological manipulations.
1 . Humphries MD, Stewart RD, Gurney KN (2006) A physiologically plausible model of action selection and oscillatory activity in the basal ganglia. J Neurosci 26:12921-42 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism: Basal ganglia;
Cell Type(s): Neostriatum spiny direct pathway neuron; Subthalamus nucleus projection neuron; Globus pallidus neuron; Abstract integrate-and-fire leaky neuron;
Gap Junctions:
Receptor(s): Dopaminergic Receptor;
Transmitter(s): Dopamine; Gaba; Glutamate;
Simulation Environment: MATLAB;
Model Concept(s): Oscillations; Parkinson's; Action Selection/Decision Making; Sleep; Rebound firing;
Implementer(s): Humphries, Mark D [m.d.humphries at shef.ac.uk];
Search NeuronDB for information about:  Neostriatum spiny direct pathway neuron; Dopaminergic Receptor; Dopamine; Gaba; Glutamate;
function [sel_results_list,a_fname,txt_fname] = batch_selection_grid(prefix,pars_file,r_seed,pathroot,exp_name)

% handles a batch of selection experiments on a single model
% Mark Humphries 27/1/2006

thresh = 5;

load input_grid
[No_sims c] = size(input_array);

batch_gpis = struct('means', {}, 'stds', {}, 'units', {}, 'switch_sum', {});
batch_sum = [];
sel_results_list = {};

for i = 1:No_sims
    fprintf(1, 'simulation %d\n', i);
    % changes this
    rfname = BATCH_BG_heterogenous_AMPA_NMDA(i,pars_file,r_seed,pathroot,exp_name);
    % keep this
    fprintf(1, 'post processing %d\n', i);
    [gpi_means, gpi_stds, GPi_ch, summary] = mean_outputs(i,rfname,thresh);
    batch_gpis(i).means = gpi_means;
    batch_gpis(i).stds = gpi_stds;
    batch_gpis(i).units = GPi_ch;
    batch_gpis(i).switch_sum = summary;
    batch_sum = [batch_sum; summary];
    sel_results_list{i} = rfname;

% save all classifications to text file...
time_now = clock;
unique_name = datestr(time_now,30);  

txt_fname = [prefix '_' unique_name '_sel_sum.txt'];
save ([pathroot txt_fname],'batch_sum','-ascii')

% save analysis to path
a_fname = [prefix '_' unique_name '_analysis.mat'];
save([pathroot a_fname],'batch_gpis','batch_sum');

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