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]
Citations  Citation Browser
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):
%THIS FILE GENERATE THE DATA FOR FIGURE 10 OF THE PAPER J.COMPUT.NEUROSCI.
%(2009), 26: 55-73, AND PLOT THE FIGURE

clear
clc
close all


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% UNISENSORY VISUAL INPUT (I = 22)

posizione_m=[20 20]; 
posizione_v=[20 20];
posizione_a=[20 20]; 
input_v=22; 
input_a=0; 
posizione_contrasto_a=[20 14]; 
posizione_contrasto_v=[20 14]; 
input_v_contrasto=0;    
input_a_contrasto=0;    

inputvisivo
inputacustico

load synapses_La
load synapses_Lv
load synapses_Lm


load inputvisivo
load inputacustico

iter=1000;        
dt=0.1;
t=[0:iter]*dt;
L=length(t);


Nv=41;
Mv=41;

Gv=1;
phiv=3;      
pend_v=0.3;       
Tv=3;                                                        

Iv = zeros(Nv,Mv,L);
xv=zeros(Nv,Mv,L);        




Na=41;
Ma=41;

Ga=1;
phia=3;      
pend_a=0.3;      
Ta=3;                                                       

Ia = zeros(Na,Ma,L);
xa=zeros(Na,Ma,L);         
                           



Nm=41;
Mm=41;

Gm=1;
phim= 3;    
pend_m=0.3;       
Tm=3;                                                       

xm=zeros(Nm,Mm,L); 

Kv=7; 
Ka=6; 


kmv=1;   
kma=1;   

Wma=cell(Nm,Mm);
for i = 1:Nm,
    for j = 1:Mm, 
        Wma{i,j}=zeros(Nm,Mm);
        Wma{i,j}(i,j)=kma;              
    end
end


Wmv=cell(Nm,Mm);
for i = 1:Nm,
    for j = 1:Mm, 
        Wmv{i,j}=zeros(Nm,Mm);
        Wmv{i,j}(i,j)=kmv;              
    end
end





time_inputV=[0 1000]; 
timeV=time_inputV+1; 

time_inputA=[0 1000]; 
timeA=time_inputA+1; 

for k=1:L-1,
    dt*k;
    k
    if k >= timeV(1) & k < timeV(2),
        Iv(:,:,k)=input_camporec_visivo;
    end
    if k >= timeA(1) & k < timeA(2),
        Ia(:,:,k)=input_camporec_acustico;              
    end
   
    
    
    
    
    for i = 1:Nv,
        for j = 1:Mv,
            
            in_laterali_v(i,j)=sum(sum(LLv{i,j}.*xv(:,:,k)));
            in_feedback_v(i,j)=sum(sum(Wmv{i,j}.*xm(:,:,k)));
            Sv(i,j)=in_laterali_v(i,j) + in_feedback_v(i,j); %  
            
        end
    end
    
    
    for i = 1:Na,
        for j = 1:Ma,
            
            in_laterali_a(i,j)=sum(sum(LLa{i,j}.*xa(:,:,k)));
            in_feedback_a(i,j)=sum(sum(Wma{i,j}.*xm(:,:,k)));
            Sa(i,j)=in_laterali_a(i,j) + in_feedback_a(i,j); %  
            
        end
    end
    

    for i = 1:Nm,
        for j = 1:Mm,
            Sm(i,j)=sum(sum(LLm{i,j}.*xm(:,:,k)));
        end
    end
    
    
    xv(:,:,k+1)=xv(:,:,k)+dt*((1/Tv)*(-xv(:,:,k)+Gv./(1+exp(-(Iv(:,:,k)+Sv-phiv)*pend_v))));
    %xv(:,:,k+1)=xv(:,:,k)+dt*((1/Tv)*(-xv(:,:,k)+Gv./(1+exp(-(Iv(:,:,k)+in_feedback_v-phiv)*pend_v)))); %eliminate lateral synapses
    %xv(:,:,k+1)=xv(:,:,k)+dt*((1/Tv)*(-xv(:,:,k)+Gv./(1+exp(-(Iv(:,:,k)-phiv)*pend_v)))); %eliminate lateral and feedback synapses      
    xv(:,:,k+1)=ceil(xv(:,:,k+1)*100000000)/100000000;
    
    xa(:,:,k+1)=xa(:,:,k)+dt*((1/Ta)*(-xa(:,:,k)+Ga./(1+exp(-(Ia(:,:,k)+Sa-phia)*pend_a))));        
    %xa(:,:,k+1)=xa(:,:,k)+dt*((1/Ta)*(-xa(:,:,k)+Ga./(1+exp(-(Ia(:,:,k)+in_feedback_a-phia)*pend_a)))); %eliminate lateral synapses
    %xa(:,:,k+1)=xa(:,:,k)+dt*((1/Ta)*(-xa(:,:,k)+Ga./(1+exp(-(Ia(:,:,k)-phia)*pend_a))));  %eliminate lateral and feeback synapses                         
    xa(:,:,k+1)=ceil(xa(:,:,k+1)*100000000)/100000000;
    
    xm(:,:,k+1)=xm(:,:,k)+dt*((1/Tm)*(-xm(:,:,k)+Gm./(1+exp(-(Kv*xv(:,:,k)+Ka*xa(:,:,k)+Sm-phim)*pend_m)))); 
    %xm(:,:,k+1)=xm(:,:,k)+dt*((1/Tm)*(-xm(:,:,k)+Gm./(1+exp(-(Kv*xv(:,:,k)+Ka*xa(:,:,k)-phim)*pend_m))));%%eliminate lateral synapses
    xm(:,:,k+1)=ceil(xm(:,:,k+1)*100000000)/100000000;
    
    input_acustico(k)=Ia(posizione_a(1),posizione_a(2),k)+Sa(posizione_a(1),posizione_a(2));
    input_laterali_a(k)=in_laterali_a(posizione_a(1),posizione_a(2));
    input_feedback_a(k)=in_feedback_a(posizione_a(1),posizione_a(2));
    
    input_visivo(k)=Iv(posizione_v(1),posizione_v(2),k)+Sv(posizione_v(1),posizione_v(2));
    input_laterali_v(k)=in_laterali_v(posizione_v(1),posizione_v(2));
    input_feedback_v(k)=in_feedback_v(posizione_v(1),posizione_v(2));
    
    input_multisensoriale(k)=Kv*xv(posizione_v(1),posizione_v(2),k)+Ka*xa(posizione_a(1),posizione_a(2),k)+Sm(posizione_m(1),posizione_m(2));         %
    input_A_multisensoriale(k)=Ka*xa(posizione_a(1),posizione_a(2),k);
    input_V_multisensoriale(k)=Kv*xv(posizione_v(1),posizione_v(2),k);
    input_laterali_multisens(k)=Sm(posizione_m(1),posizione_m(2));
    

end

for k=1:length(t),
    xvplot_uni(k)=xv(posizione_v(1),posizione_v(2),k);
    xaplot_uni(k)=xa(posizione_a(1),posizione_a(2),k);
    xmplot_uni(k)=xm(posizione_m(1),posizione_m(2),k);
end


save Fig10 t xmplot_uni

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% MULTISENSORY INPUT: VISUAL AND ACOUSTIC INPUT, EACH OF INTENSITY I = 22
clear

posizione_m=[20 20]; 
posizione_v=[20 20]; 
posizione_a=[20 20]; 
input_v=22; 
input_a=22; 
posizione_contrasto_a=[20 14]; 
posizione_contrasto_v=[20 14]; 
input_v_contrasto=0;   
input_a_contrasto=0;    

inputvisivo
inputacustico

load synapses_La
load synapses_Lv
load synapses_Lm


load inputvisivo
load inputacustico

iter=1000;        
dt=0.1;
t=[0:iter]*dt;
L=length(t);


Nv=41;
Mv=41;

Gv=1;
phiv=3       
pend_v=0.3;       
Tv=3;                                                        

Iv = zeros(Nv,Mv,L);
xv=zeros(Nv,Mv,L);         
                          




Na=41;
Ma=41;

Ga=1;
phia=3;      
pend_a=0.3;      
Ta=3;                                                        

Ia = zeros(Na,Ma,L);
xa=zeros(Na,Ma,L);         
                           



Nm=41;
Mm=41;

Gm=1;
phim= 3;      
pend_m=0.3;       
Tm=3;                                                      

xm=zeros(Nm,Mm,L); 

Kv=7; 
Ka=6; 


kmv=1;     
kma=1;   

Wma=cell(Nm,Mm);
for i = 1:Nm,
    for j = 1:Mm, 
        Wma{i,j}=zeros(Nm,Mm);
        Wma{i,j}(i,j)=kma;             
    end
end


Wmv=cell(Nm,Mm);
for i = 1:Nm,
    for j = 1:Mm, 
        Wmv{i,j}=zeros(Nm,Mm);
        Wmv{i,j}(i,j)=kmv;              
    end
end





time_inputV=[0 1000]; 
timeV=time_inputV+1; 

time_inputA=[0 1000]; 
timeA=time_inputA+1; 

for k=1:L-1,
    dt*k;
    k
    if k >= timeV(1) & k < timeV(2),
        Iv(:,:,k)=input_camporec_visivo;
    end
    if k >= timeA(1) & k < timeA(2),
        Ia(:,:,k)=input_camporec_acustico;              
    end
    
    
    
    
    for i = 1:Nv,
        for j = 1:Mv,
            
            in_laterali_v(i,j)=sum(sum(LLv{i,j}.*xv(:,:,k)));
            in_feedback_v(i,j)=sum(sum(Wmv{i,j}.*xm(:,:,k)));
            Sv(i,j)=in_laterali_v(i,j) + in_feedback_v(i,j); %  
            
        end
    end
    
    
    for i = 1:Na,
        for j = 1:Ma,
            
            in_laterali_a(i,j)=sum(sum(LLa{i,j}.*xa(:,:,k)));
            in_feedback_a(i,j)=sum(sum(Wma{i,j}.*xm(:,:,k)));
            Sa(i,j)=in_laterali_a(i,j) + in_feedback_a(i,j); %  
            
        end
    end
    

    for i = 1:Nm,
        for j = 1:Mm,
            Sm(i,j)=sum(sum(LLm{i,j}.*xm(:,:,k)));
        end
    end
    
    
    xv(:,:,k+1)=xv(:,:,k)+dt*((1/Tv)*(-xv(:,:,k)+Gv./(1+exp(-(Iv(:,:,k)+Sv-phiv)*pend_v))));
    %xv(:,:,k+1)=xv(:,:,k)+dt*((1/Tv)*(-xv(:,:,k)+Gv./(1+exp(-(Iv(:,:,k)+in_feedback_v-phiv)*pend_v)))); %eliminate lateral synapses 
    %xv(:,:,k+1)=xv(:,:,k)+dt*((1/Tv)*(-xv(:,:,k)+Gv./(1+exp(-(Iv(:,:,k)-phiv)*pend_v)))); %eliminate lateral and feedback synapses      
    xv(:,:,k+1)=ceil(xv(:,:,k+1)*100000000)/100000000;
    
    xa(:,:,k+1)=xa(:,:,k)+dt*((1/Ta)*(-xa(:,:,k)+Ga./(1+exp(-(Ia(:,:,k)+Sa-phia)*pend_a))));      
    %xa(:,:,k+1)=xa(:,:,k)+dt*((1/Ta)*(-xa(:,:,k)+Ga./(1+exp(-(Ia(:,:,k)+in_feedback_a-phia)*pend_a)))); %eliminate lateral synapses
    %xa(:,:,k+1)=xa(:,:,k)+dt*((1/Ta)*(-xa(:,:,k)+Ga./(1+exp(-(Ia(:,:,k)-phia)*pend_a))));  %eliminate lateral and feedback synapses                          
    xa(:,:,k+1)=ceil(xa(:,:,k+1)*100000000)/100000000;
    
    xm(:,:,k+1)=xm(:,:,k)+dt*((1/Tm)*(-xm(:,:,k)+Gm./(1+exp(-(Kv*xv(:,:,k)+Ka*xa(:,:,k)+Sm-phim)*pend_m))));
    %xm(:,:,k+1)=xm(:,:,k)+dt*((1/Tm)*(-xm(:,:,k)+Gm./(1+exp(-(kv*xv(:,:,k)+ka*xa(:,:,k)-phim)*pend_m))));%eliminate lateral synapses
    xm(:,:,k+1)=ceil(xm(:,:,k+1)*100000000)/100000000;
    
    input_acustico(k)=Ia(posizione_a(1),posizione_a(2),k)+Sa(posizione_a(1),posizione_a(2));
    input_laterali_a(k)=in_laterali_a(posizione_a(1),posizione_a(2));
    input_feedback_a(k)=in_feedback_a(posizione_a(1),posizione_a(2));
    
    input_visivo(k)=Iv(posizione_v(1),posizione_v(2),k)+Sv(posizione_v(1),posizione_v(2));
    input_laterali_v(k)=in_laterali_v(posizione_v(1),posizione_v(2));
    input_feedback_v(k)=in_feedback_v(posizione_v(1),posizione_v(2));
    
    input_multisensoriale(k)=Kv*xv(posizione_v(1),posizione_v(2),k)+Ka*xa(posizione_a(1),posizione_a(2),k)+Sm(posizione_m(1),posizione_m(2));         %
    input_A_multisensoriale(k)=Ka*xa(posizione_a(1),posizione_a(2),k);
    input_V_multisensoriale(k)=Kv*xv(posizione_v(1),posizione_v(2),k);
    input_laterali_multisens(k)=Sm(posizione_m(1),posizione_m(2));
    

end

for k=1:length(t),
    xvplot_multi(k)=xv(posizione_v(1),posizione_v(2),k);
    xaplot_multi(k)=xa(posizione_a(1),posizione_a(2),k);
    xmplot_multi(k)=xm(posizione_m(1),posizione_m(2),k);
end

save Fig10 xmplot_multi -append

clear
load Fig10
figure(1)
plot(t,xmplot_uni,'--k','linewidth',2)
hold on
plot(t,xmplot_multi,'-k','linewidth',2)
xlabel('time(ms)')
ylabel('SCN normalized activity')
set(gca,'fontsize',18)