A model of working memory for encoding multiple items (Ursino et al, in press)

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Accession:267297
We present an original neural network model, based on oscillating neural masses, to investigate mechanisms at the basis of working memory in different conditions. Simulations show that the trained network is able to desynchronize up to nine items without a fixed order using the gamma rhythm. Moreover, the network can replicate a sequence of items using a gamma rhythm nested inside a theta rhythm. The reduction in some parameters, mainly concerning the strength of GABAergic synapses, induce memory alterations which mimic neurological deficits. Finally, the network, isolated from the external environment simulates an“imagination phase”.
Reference:
1 . Ursino M, Cesaretti N, Pirazzini G (in press) A model of working memory for encoding multiple items and ordered sequences exploiting the theta-gamma code Cognitive Neurodynamics
Model Information (Click on a link to find other models with that property)
Model Type: Neural mass; Synapse; Realistic Network;
Brain Region(s)/Organism:
Cell Type(s):
Channel(s):
Gap Junctions: Gap junctions;
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: MATLAB;
Model Concept(s): Gamma oscillations;
Implementer(s): Ursino, Mauro [mauro.ursino at unibo.it];
%% parametri
dt=0.0001; %0.1 millisecondi
t_end3=0.15;
t=0:dt:t_end3;
T=length(t);

% Parametri sigmoide
e0=2.5; %Hz
r=0.7; %1/mV
s0=10; %centro della sigmoide

% ritardi nella comunicazione tra colonne diverse
D_intraLayer=round(dt/dt); 
D_extraLayer=round(dt/dt); %delay di 0.0166 secondi ?
                
% costanti di tempo sinapsi intra-colonna
a=[1/7.7 1/34 1/6.8]*1000; %nell'ordine: ae, as, af; a=1/tau (1/secondi)

% Guadagni (mV) delle sinapsi (G)
G=[5.17 4.45 57.1]; %Ge = 5.17; (per h_e)
                    %Gs = 4.45; (per h_s)
                    %Gf = 57.1; (per h_f)

%pesi sinaptici:
C(:,1) = 31.7*ones(1,numero_colonne); %Cep
C(:,2) = 17.3*ones(1,numero_colonne); %Cpe  
C(:,3) = 51.9*ones(1,numero_colonne); %Csp
C(:,4) = 100*ones(1,numero_colonne); %Cps 
C(:,5) = 100*ones(1,numero_colonne); %Cfs
C(:,6) = 66.9*ones(1,numero_colonne); %Cfp 
C(:,7) = 16*ones(1,numero_colonne); %Cpf
C(:,8) = 18*ones(1,numero_colonne); %Cff

%% addestramento
Wp_L2L3=zeros(numero_colonne,numero_colonne);
gammaWb=10;
thresh_lowb=0.7;
Wp_L2L3_max=11;
%var stato L2
yp2=zeros(numero_colonne,T);
xp2=zeros(numero_colonne,T);
vp2=zeros(numero_colonne,1); 
zp2=zeros(numero_colonne,T);

ye2=zeros(numero_colonne,T);
xe2=zeros(numero_colonne,T);
ve2=zeros(numero_colonne,1);
ze2=zeros(numero_colonne,T);

ys2=zeros(numero_colonne,T);
xs2=zeros(numero_colonne,T);
vs2=zeros(numero_colonne,1);
zs2=zeros(numero_colonne,T);

yf2=zeros(numero_colonne,T);
xf2=zeros(numero_colonne,T);
zf2=zeros(numero_colonne,T);
vf2=zeros(numero_colonne,1);

xl2=zeros(numero_colonne,T);
yl2=zeros(numero_colonne,T);

mf2=zeros(numero_colonne,1);

%var stato L3
yp3=zeros(numero_colonne,T);
xp3=zeros(numero_colonne,T);
vp3=zeros(numero_colonne,1); 
zp3=zeros(numero_colonne,T);

ye3=zeros(numero_colonne,T);
xe3=zeros(numero_colonne,T);
ve3=zeros(numero_colonne,1);
ze3=zeros(numero_colonne,T);

ys3=zeros(numero_colonne,T);
xs3=zeros(numero_colonne,T);
vs3=zeros(numero_colonne,1);
zs3=zeros(numero_colonne,T);

yf3=zeros(numero_colonne,T);
xf3=zeros(numero_colonne,T);
zf3=zeros(numero_colonne,T);
vf3=zeros(numero_colonne,1);

xl3=zeros(numero_colonne,T);
yl3=zeros(numero_colonne,T);

mf3=zeros(numero_colonne,1); 

Ep2=zeros(numero_colonne,1);
If2=zeros(numero_colonne,1);
Ep3=zeros(numero_colonne,1);
If3=zeros(numero_colonne,1);

sigma_p = sqrt(5/dt);
sigma_f = sqrt(5/dt);
rng(12)  
np2 = randn(numero_colonne,T)*sigma_p;
nf2 = randn(numero_colonne,T)*sigma_f;
rng(13)
np3 = randn(numero_colonne,T)*sigma_p;
nf3 = randn(numero_colonne,T)*sigma_f;

if SET_PATT==3
    SEQ2=all_patterns(:,[2:5 7:10]);
    SEQ3=[all_patterns(:,1:4) all_patterns(:,6:9)];
else
    SEQ2=all_patterns;
    SEQ3=[zeros(numero_colonne,1) all_patterns(:,1:end-1)];
end

J=size(SEQ2,2);
w = waitbar(0,'Training L2 e L3 (fase 2)...','WindowStyle','modal');
for j=1:J
    %completo ingressi a piramidali e gaba fast:
        mp2=SEQ2(:,j)*2700; 
        mp3=SEQ3(:,j)*2700;

    for k=1:T-1 %ciclo nel tempo... 
        up2=np2(:,k)+mp2;
        uf2=nf2(:,k)+mf2;
        up3=np3(:,k)+mp3;
        uf3=nf3(:,k)+mf3;

        if (k>D_intraLayer)
            If2=K_L2L2*yp2(:,k-D_intraLayer)+A_L2L2*zp2(:,k-D_intraLayer);
            If3=K_L3L3*yp3(:,k-D_intraLayer)+A_L3L3*zp3(:,k-D_intraLayer);
        end
        
        if(k>D_extraLayer)
            Ep2=Wp_L2L3*yp3(:,k-D_extraLayer);
            %Ep3=Wp_L3L2*yp2(:,k-D_extraLayer);
            %input extra-layer solo dal maestro!
        end
        
        %potenziali post-sinaptici: (comb lin degli outputs standard)
        vp2(:)=C(:,2).*ye2(:,k)-C(:,4).*ys2(:,k)-C(:,7).*yf2(:,k)+Ep2;
        ve2(:)=C(:,1).*yp2(:,k);
        vs2(:)=C(:,3).*yp2(:,k);
        vf2(:)=C(:,6).*yp2(:,k)-C(:,5).*ys2(:,k)-C(:,8).*yf2(:,k)+yl2(:,k)+If2;
        %spikes:
        zp2(:,k)=2*e0./(1+exp(-r*(vp2(:)-s0)));
        ze2(:,k)=2*e0./(1+exp(-r*(ve2(:)-s0)));
        zs2(:,k)=2*e0./(1+exp(-r*(vs2(:)-s0)));
        zf2(:,k)=2*e0./(1+exp(-r*(vf2(:)-s0)));
        
        %potenziali post-sinaptici: (comb lin degli outputs standard)
        vp3(:)=C(:,2).*ye3(:,k)-C(:,4).*ys3(:,k)-C(:,7).*yf3(:,k)+Ep3;
        ve3(:)=C(:,1).*yp3(:,k);
        vs3(:)=C(:,3).*yp3(:,k);
        vf3(:)=C(:,6).*yp3(:,k)-C(:,5).*ys3(:,k)-C(:,8).*yf3(:,k)+yl3(:,k)+If3;
        %spikes:
        zp3(:,k)=2*e0./(1+exp(-r*(vp3(:)-s0)));
        ze3(:,k)=2*e0./(1+exp(-r*(ve3(:)-s0)));
        zs3(:,k)=2*e0./(1+exp(-r*(vs3(:)-s0)));
        zf3(:,k)=2*e0./(1+exp(-r*(vf3(:)-s0)));
        
        %aggiornamento sinapsi...
        if k>500
            ATT_PRE=(zp3(:,k)/(2*e0) - thresh_lowb)'; %1x400
            ATT_PRE(ATT_PRE<0)=0;
            ATT_POST=(zp2(:,k)/(2*e0)-thresh_lowb); %400x1
            ATT_POST(ATT_POST<0)=0;
            WEIGHT=(Wp_L2L3_max - Wp_L2L3).*(ones(numero_colonne, numero_colonne)-eye(numero_colonne));
            Wp_L2L3 = Wp_L2L3 + gammaWb .* (ATT_POST * ATT_PRE) .* WEIGHT;
        end
        
        xp2(:,k+1)=xp2(:,k)+(G(1)*a(1)*zp2(:,k)-2*a(1)*xp2(:,k)-a(1)*a(1)*yp2(:,k))*dt;
        yp2(:,k+1)=yp2(:,k)+xp2(:,k)*dt;
        xe2(:,k+1)=xe2(:,k)+(G(1)*a(1)*(ze2(:,k)+up2(:)./C(:,2))-2*a(1)*xe2(:,k)-a(1)*a(1)*ye2(:,k))*dt;
        ye2(:,k+1)=ye2(:,k)+xe2(:,k)*dt;
        xs2(:,k+1)=xs2(:,k)+(G(2)*a(2)*zs2(:,k)-2*a(2)*xs2(:,k)-a(2)*a(2)*ys2(:,k))*dt;
        ys2(:,k+1)=ys2(:,k)+xs2(:,k)*dt;
        xl2(:,k+1)=xl2(:,k)+(G(1)*a(1)*uf2(:)-2*a(1)*xl2(:,k)-a(1)*a(1)*yl2(:,k))*dt;
        yl2(:,k+1)=yl2(:,k)+xl2(:,k)*dt;
        xf2(:,k+1)=xf2(:,k)+(G(3)*a(3)*zf2(:,k)-2*a(3)*xf2(:,k)-a(3)*a(3)*yf2(:,k))*dt;
        yf2(:,k+1)=yf2(:,k)+xf2(:,k)*dt;
        
        xp3(:,k+1)=xp3(:,k)+(G(1)*a(1)*zp3(:,k)-2*a(1)*xp3(:,k)-a(1)*a(1)*yp3(:,k))*dt;
        yp3(:,k+1)=yp3(:,k)+xp3(:,k)*dt;
        xe3(:,k+1)=xe3(:,k)+(G(1)*a(1)*(ze3(:,k)+up3(:)./C(:,2))-2*a(1)*xe3(:,k)-a(1)*a(1)*ye3(:,k))*dt;
        ye3(:,k+1)=ye3(:,k)+xe3(:,k)*dt;
        xs3(:,k+1)=xs3(:,k)+(G(2)*a(2)*zs3(:,k)-2*a(2)*xs3(:,k)-a(2)*a(2)*ys3(:,k))*dt;
        ys3(:,k+1)=ys3(:,k)+xs3(:,k)*dt;
        xl3(:,k+1)=xl3(:,k)+(G(1)*a(1)*uf3(:)-2*a(1)*xl3(:,k)-a(1)*a(1)*yl3(:,k))*dt;
        yl3(:,k+1)=yl3(:,k)+xl3(:,k)*dt;
        xf3(:,k+1)=xf3(:,k)+(G(3)*a(3)*zf3(:,k)-2*a(3)*xf3(:,k)-a(3)*a(3)*yf3(:,k))*dt;
        yf3(:,k+1)=yf3(:,k)+xf3(:,k)*dt;
        
    end        
    waitbar(j/J,w);
end
close(w)

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