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):
% makeall_populations defines all the background parameters and the size of 
% populations within for the simulation of the FEF network
%
% created: Jakob Heinzle 01/07

%-------------------------------------------------------------------------
% Define populations within the FEF.
%-------------------------------------------------------------------------
%background input feature selection neurons
gmaxE_ext = 0.02; %
gmaxI_ext = 3*gmaxE_ext;

% Add populations in Layer 4.
name='E4';type='exc';poolsize=100;nretpos=21;bgE=0.472;bgI=0.34;
add_population;
name='I4';type='inh';poolsize=25;nretpos=21;bgE=0.46;bgI=0.4;
add_population;

% Add populations in Layer 2/3.
name='E23';type='exc';poolsize=100;nretpos=21;bgE=0.472;bgI=0.34;
add_population;
name='I23';type='inh';poolsize=25;nretpos=21;bgE=0.46;bgI=0.4;
add_population;

% Add populations in Layer 5.
name='E5R';type='exc';poolsize=40;nretpos=21;bgE=0.45;bgI=0.34;
add_population;
name='I5R';type='inh';poolsize=25;nretpos=21;bgE=0.42;bgI=0.34;
add_population;

name='E5B';type='exc';poolsize=40;nretpos=21;bgE=0.38;bgI=0.30;
add_population;
name='I5B';type='inh';poolsize=25;nretpos=21;bgE=0.32;bgI=0.34;
add_population;

% Add populations in Layer 6.
name='E6S';type='exc';poolsize=50;nretpos=21;bgE=0.44;bgI=0.34;
add_population;
name='E6A';type='exc';poolsize=50;nretpos=21;bgE=0.2;bgI=0.34;
add_population;

% Add Fixation neurons
name='IFIX';type='inh';poolsize=100;nretpos=1;bgE=0.46;bgI=0.12;
add_population;


%-------------------------------------------------------------------------
% define populations of the Recognition Module.
%-------------------------------------------------------------------------

% Add inhibory neurons in feature detection
name='IF';type='inh';poolsize=25;nretpos=21;bgE=0.55;bgI=0.34;
add_population;

% Add feature detection arrays.
name='EFp';type='inh';poolsize=100;nretpos=21;bgE=0.42;bgI=0.30; %Pro-Saccade
add_population;
name='EFf';type='inh';poolsize=100;nretpos=21;bgE=0.42;bgI=0.30; %Fixation
add_population;
name='EFa';type='inh';poolsize=100;nretpos=21;bgE=0.42;bgI=0.30; %Anti-Saccade
add_population;
name='EFspace';type='inh';poolsize=100;nretpos=1;bgE=0.42;bgI=0.30; %Recognizing spaces
add_population;

% Add recognition arrays
name='ERr';type='exc';poolsize=100;nretpos=21;bgE=0.45;bgI=0.33;
add_population;
name='ERb';type='exc';poolsize=100;nretpos=21;bgE=0.38;bgI=0.30;
add_population;
name='IRb';type='inh';poolsize=25;nretpos=21;bgE=0.32;bgI=0.34;
add_population;