A microcircuit model of the frontal eye fields (Heinzle et al. 2007)

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Accession:110022
" ... we show that the canonical circuit (Douglas et al. 1989, Douglas and Martin 1991) can, with a few modifications, model the primate FEF. The spike-based network of integrate-and-fire neurons was tested in tasks that were used in electrophysiological experiments in behaving macaque monkeys. The dynamics of the model matched those of neurons observed in the FEF, and the behavioral results matched those observed in psychophysical experiments. The close relationship between the model and the cortical architecture allows a detailed comparison of the simulation results with physiological data and predicts details of the anatomical circuit of the FEF."
Reference:
1 . Heinzle J, Hepp K, Martin KA (2007) A microcircuit model of the frontal eye fields. J Neurosci 27:9341-53 [PubMed]
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Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism: Neocortex;
Cell Type(s):
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: MATLAB;
Model Concept(s): Spatio-temporal Activity Patterns; Action Selection/Decision Making; Vision;
Implementer(s):
% add_population adds a new population to the FEF network
% the following paramters need to be defined earlier:
% name: name of the population
% type: 'exc' (excitatory) or 'inh' (inhibitory)
% poolsize: size of one population in the array
% nretpos: number of retinotopic positions
% bgE: mean excitatory background input.
% bgI: mean inhibitory background input.
%
% created: Jakob Heinzle 01/07

n=pops.npops+1;

% general information about the population.
pops.population{n}.name=name;
pops.population{n}.type=type;
pops.population{n}.poolsize=poolsize;
pops.population{n}.nretpos=nretpos;
pops.population{n}.bgExc=bgE;
pops.population{n}.bgInh=bgI;
pops.population{n}.NoiseExc=sqrt(gmaxE_ext*pops.population{n}.bgExc/2);
pops.population{n}.NoiseInh=sqrt(gmaxI_ext*pops.population{n}.bgInh/2);

% auxiliary variables.
n_neurons=pops.population{n}.nretpos*pops.population{n}.poolsize;
pops.population{n}.n_neurons=n_neurons;
pops.population{n}.tspk=zeros(n_neurons,1);
pops.population{n}.Vm=5*rand(n_neurons,1);
pops.population{n}.spikes=zeros(n_neurons,1);
pops.population{n}.refrac = zeros(n_neurons,1);
pops.population{n}.inEAux = ones(n_neurons,1)*pops.population{n}.bgExc;
pops.population{n}.inIAux = ones(n_neurons,1)*pops.population{n}.bgInh;

% information added later, when connections and external inputs are added.
pops.population{n}.input_external=[]; %define, which external inputs, target the population
pops.population{n}.input_exc=[]; %define, which internal conductances, target the population
pops.population{n}.input_inh=[]; %define, which internal conductances, target the population

pops.npops=n;