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):
function ys = smooth_synapse(y,trise,tdecay)

% smooth_synapse: smoothing of spike signal with a synaptic kernel
% Convolves the vector y with a synaptic double exponential kernel 
% (1-exp(-t/trise)).*exp(-t/tdecay). This smoothing procedure keeps  the 
% causality in the reponses.
% 
% created: Jakob Heinzle 01/07

y = makecolumn(y);

% Compute synaptic kernel
 Nxs = ceil(3*tdecay);
 
 xs = [-Nxs:Nxs]';
 g  = (1-exp(-xs/trise)).*exp(-xs/tdecay);
 g(find(xs<=0))=0;
 g  = g/sum(g);            
 % Add a mirror image at beginning and end to avoid edge effects.
 lo_edge = y(Nxs:-1:1);
 hi_edge = y(end:-1:end-Nxs+1);
 y = [lo_edge; y; hi_edge];
 
 % compute the convolution of the vectors
     yst = conv(y,g);        % convolve the 'spikes' with the kernel
     Nc1 = length(yst);           
     ys = yst(2*Nxs+1:Nc1-2*Nxs+1);    % resize smoothed vector