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

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" ... 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."
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):
Gap Junctions:
Simulation Environment: MATLAB;
Model Concept(s): Spatio-temporal Activity Patterns; Action Selection/Decision Making; Vision;
% DefineParameters defines some general parameters for the simulation
% of the FEF network.
% created: Jakob Heinzle 01/07

dt    = 0.10;           % integration time step in ms
gfac  = 2;              % factor for weights calculated as poolsize*0.02              

% Cell parameters; here rest is V=0. Adapted from Salinas, Neural
% Computation

VeqE    = 74;       % 74 mV for AMPA
VeqI    = -10;      % from -20 (K) to 0 (Cl, shunting)
V_th    = 20;       % spike threshold
V_reset = 10;       % -60 from Troyer and Miller, 1997
taucorrE= 3.0;      % time constant for background E input
taucorrI= 3.0;      % time constant for background I input
tauME   = 20;       % in milliseconds; 20 from McCormick
tauMI   = 12;       % in milliseconds; 12 from McCormick
trefE   = 1.8;      % refractory period in ms
trefI   = 1.2;      % refractory period in ms


% integration time steps
tstepEc = exp(-dt/taucorrE);
tstepEc1= 1 - tstepEc;
tstepEc2= sqrt(1 - tstepEc^2);
tstepIc = exp(-dt/taucorrI);
tstepIc1= 1 - tstepIc;
tstepIc2= sqrt(1 - tstepIc^2);

% number of iterations
iterations = ceil(tmax/dt);

% define a series of auxiliaries          
jskip=skip+1; jspkE=0; jspkI=0; % parameters used for checking for saccade and for graphics update.
recdt=-dt/2; ind=1; facE=1000/100;  facI=facE*4; % ausxiliary values used to store rates of the neurons.