Multisensory integration in the superior colliculus: a neural network model (Ursino et al. 2009)

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Accession:118261
" ... The model includes three distinct neural areas: two unimodal areas (auditory and visual) are devoted to a topological representation of external stimuli, and communicate via synaptic connections with a third downstream area (in the SC) responsible for multisensory integration. The present simulations show that the model, with a single set of parameters, can mimic various responses to different combinations of external stimuli including the inverse effectiveness, both in terms of multisensory enhancement and contrast, the existence of within- and cross-modality suppression between spatially disparate stimuli, a reduction of network settling time in response to cross-modal stimuli compared with individual stimuli. ..."
Reference:
1 . Ursino M, Cuppini C, Magosso E, Serino A, di Pellegrino G (2009) Multisensory integration in the superior colliculus: a neural network model. J Comput Neurosci 26:55-73 [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: Superior colliculus;
Cell Type(s):
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: MATLAB;
Model Concept(s): Activity Patterns; Vision; Audition;
Implementer(s):
% enter intensities of external stimuli to the neuron in position [20 14]
% (the TARGET NEURON); enter the position of ANTAGONIST STIMULI and their
% intensities.
% this program computes the activities in the 3 areas of the model in
% response to the configuration of external stimuli given as inputs

global posizione_v posizione_a posizione_contrasto_a posizione_contrasto_v posizione_m


posizione_m=[20 14];
posizione_v=[20 14];
posizione_a=[20 14];
input_v=input('intensity of the visual input to the neuron in [20 14]:\n');
input_a=input('intensity of the auditory input to the neuron in [20 14]:\n');
posizione_contrasto_a=input('enter, in square brackets, the position of the antagonist auditory neuron:\n');
posizione_contrasto_v=input('enter, in square brackets, the position of the antagonist visual neuron:\n');
input_v_contrasto=input('intensity of the visual input to the antagonist neuron:\n');
input_a_contrasto=input('intensity of the auditory input to the antagonist neuron:\n');

% generation of the inputs to the unimodal areas

inputvisivo
inputacustico

% generation of the intra-area synapses

L_auditory
L_visual
L_SC

clear

load synapses_La
load synapses_Lv
load synapses_Lm

% running the model
rete2D_A_V_SC

% GRAPHS

for k=1:length(t),
    xvplot(k)=xv(posizione_v(1),posizione_v(2),k);
    xaplot(k)=xa(posizione_a(1),posizione_a(2),k);
    xmplot(k)=xm(posizione_m(1),posizione_m(2),k);
end

for k=1:length(t),
    xv2plot(k)=xv(posizione_contrasto_v(1),posizione_contrasto_v(2),k);
    xa2plot(k)=xa(posizione_contrasto_a(1),posizione_contrasto_a(2),k);
    xm2plot(k)=xm(posizione_contrasto_a(1),posizione_contrasto_a(2),k);
end

figure

subplot(3,2,1)
plot(t,xmplot)
grid
xlabel('Time [ms]')
ylabel('SC neuron activity')
title('target neuron');

subplot(3,2,3)
plot(t,xaplot)
grid
xlabel('Time [ms]')
ylabel('auditory neuron activity')

subplot(3,2,5)
plot(t,xvplot)
grid
xlabel('Time [ms]')
ylabel('visual neuron activity')

subplot(3,2,2)
plot(t,xm2plot)
grid
xlabel('Time [ms]')
ylabel('SC neuron activity')
title('antagonist neuron');

subplot(3,2,4)
plot(t,xa2plot)
grid
xlabel('Time [ms]')
ylabel('auditory neuron activity')

subplot(3,2,6)
plot(t,xv2plot)
grid
xlabel('Time [ms]')
ylabel('visual neuron activity')

figure
for l=1:5:L-1
    subplot(3,1,1)
    pcolor(xm(:,:,l))
    caxis([0 1])
    opengl neverselect
    ylabel('SC AREA')
    
    subplot(3,1,2)
    pcolor(xv(:,:,l))
    caxis([0 1])
    opengl neverselect
    ylabel('visual AREA')

    subplot(3,1,3)
    pcolor(xa(:,:,l))
    caxis([0 1])
    opengl neverselect
    ylabel('auditory AREA')
   
    pause
end