Surround Suppression in V1 via Withdraw of Balanced Local Excitation in V1 (Shushruth 2012)

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Accession:144096
The model is mean-field network models, which is set up as a so-called ring-model, i. e. it is a highly idealized model of an orientation hypercolumn in primary visual cortex. Long-range intra-areal and inter-areal feedback connections are modeled phenomenologically as an external input. In this model, there are recurrent interactions via short-range local connections between orientation columns, but not between hypercolumns.
Reference:
1 . Shushruth S, Mangapathy P, Ichida JM, Bressloff PC, Schwabe L, Angelucci A (2012) Strong recurrent networks compute the orientation tuning of surround modulation in the primate primary visual cortex. J Neurosci 32:308-21 [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:
Cell Type(s): Neocortex V1 L2/6 pyramidal intratelencephalic GLU cell; Neocortex V1 interneuron basket PV GABA cell;
Channel(s):
Gap Junctions:
Receptor(s): GabaA; AMPA;
Gene(s):
Transmitter(s): Gaba; Glutamate;
Simulation Environment: MATLAB;
Model Concept(s): Vision;
Implementer(s):
Search NeuronDB for information about:  Neocortex V1 L2/6 pyramidal intratelencephalic GLU cell; Neocortex V1 interneuron basket PV GABA cell; GabaA; AMPA; Gaba; Glutamate;
% ----------------------------------------------------------------------
% This script generates a figure for the model exploration.
%
% 31/8/2011,    Initial revision created
%               Lars Schwabe (lars.schwabe@uni-rostock.de)
% ----------------------------------------------------------------------

%% Load the data.
clear all;
close all;

path( path, fullfile('.','Funs') );

load( fullfile('Data','dataExploration.mat') );


%% Generate the figures.

% ----------------------------------------------------------------------
% Figure 1
% ----------------------------------------------------------------------
figure(1);

ROWS = 2;
COLS = 2;

subplot( ROWS, COLS, spos(COLS,1,1,1,1) );
hold on;
for iW = 1:nW
    MAX = max( mSurResponse1(iW,:) );
    plot( R.vSur, mSurResponse1(iW,:) ./ MAX, 'k-' );
end
set( gca, 'YLim', [0 1], 'XLim', [0 R.vSur(end)] );
set( gca, 'XTick', [0:30:180] );
line( [90 90], [0 1], 'Color', 'k' );
annotation( 'arrow', [0.27 0.20], [0.85 0.65] );
hold off;
title( 'Weakly tuned LCs (kappa=0.2)' );
xlabel( 'Surround Ori [deg]' );
ylabel( 'Ctr response, PO=90 deg [norm.]' );

subplot( ROWS, COLS, spos(COLS,2,1,1,1) );
hold on;
for iW = 1:nW
    MAX = max( mSurResponse1(iW,:) );
    plot( R.vSur, mSurResponse2(iW,:) ./ MAX, 'k-' );
end
set( gca, 'YLim', [0 1], 'XLim', [0 R.vSur(end)] );
set( gca, 'XTick', [0:30:180] );
line( [90 90], [0 1], 'Color', 'k' );
annotation( 'arrow', [0.80 0.80], [0.85 0.65] );
hold off;
title( 'Tuned LCs (kappa=2)' );
xlabel( 'Surround Ori [deg]' );
ylabel( 'Ctr response, PO=90 deg [norm.]' );

%% Print the figures into a file.
print( '-depsc2', fullfile('Figs','figExploration.eps'), '-f1' );
print( '-dpng', fullfile('Figs','figExploration.png'), '-f1' );