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
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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]; 
% WARNING
% codice dipendente da L1_main. Richiede la presenza nel workspace di
% Wp_L1L1, t_sim, INPUT_L1

%% parametri
dt=0.0001; %0.1 millisecondi
if train_flag
    t=(0:dt:t_end1);
else
    t_sim=1.5;
    t=(0:dt:t_sim);
end
T=length(t);

rng(11)  % stabilisco un certo seme per ripetere le simulazioni successive
sigma_p = sqrt(5/dt);
sigma_f = sqrt(5/dt);
np = randn(numero_colonne,T)*sigma_p;
nf = randn(numero_colonne,T)*sigma_f;

% 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);
                
% 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

%%
yp1=zeros(numero_colonne,T);
xp1=zeros(numero_colonne,T);
vp1=zeros(numero_colonne,1); 
zp1=zeros(numero_colonne,T);

ye1=zeros(numero_colonne,T);
xe1=zeros(numero_colonne,T);
ve1=zeros(numero_colonne,1);
ze1=zeros(numero_colonne,T);

ys1=zeros(numero_colonne,T);
xs1=zeros(numero_colonne,T);
vs1=zeros(numero_colonne,1);
zs1=zeros(numero_colonne,T);

yf1=zeros(numero_colonne,T);
xf1=zeros(numero_colonne,T);
zf1=zeros(numero_colonne,T);
vf1=zeros(numero_colonne,1);

xl1=zeros(numero_colonne,T);
yl1=zeros(numero_colonne,T);

Ep=zeros(numero_colonne,1);
mf1=zeros(numero_colonne,1); %media input gabaFast

% TOTSINW=130;
% buff=Wp_L1L1; 

for k=1:T-1 %ciclo nel tempo...
    %completo ingressi a piramidali e gaba fast:
    if size(INPUT_L1,2)==1
        mp1=INPUT_L1*600; 
%         S=sum(INPUT_L1>0);
%         Wp_L1L1(Wp_L1L1>0)=TOTSINW/S;
    else
        mp1=INPUT_L1(:,k)*600; %se ho dato un ingresso variabile nel tempo
%         S=sum(INPUT_L1(:,k)>0);
%         if S>0
%             Wp_L1L1(Wp_L1L1>0)=TOTSINW/S;
%         end
    end
    up1=np(:,k)+mp1;
    uf1=nf(:,k)+mf1;
    
    if(k>D_intraLayer) %se il tempo trascorso è maggiore del delay tra colonne...
        Ep=Wp_L1L1*yp1(:,k-D_intraLayer);
    end
    
    %potenziali post-sinaptici: (comb lin degli outputs standard)
    vp1(:)=C(:,2).*ye1(:,k)-C(:,4).*ys1(:,k)-C(:,7).*yf1(:,k)+Ep;
    ve1(:)=C(:,1).*yp1(:,k);
    vs1(:)=C(:,3).*yp1(:,k);
    vf1(:)=C(:,6).*yp1(:,k)-C(:,5).*ys1(:,k)-C(:,8).*yf1(:,k)+yl1(:,k);
    %spikes:
    zp1(:,k)=2*e0./(1+exp(-r*(vp1(:)-s0)));
    ze1(:,k)=2*e0./(1+exp(-r*(ve1(:)-s0)));
    zs1(:,k)=2*e0./(1+exp(-r*(vs1(:)-s0)));
    zf1(:,k)=2*e0./(1+exp(-r*(vf1(:)-s0)));
        
    %nuovi outputs "standard" (non pesati) popolazioni:
    xp1(:,k+1)=xp1(:,k)+(G(1)*a(1)*zp1(:,k)-2*a(1)*xp1(:,k)-a(1)*a(1)*yp1(:,k))*dt;
    yp1(:,k+1)=yp1(:,k)+xp1(:,k)*dt;
    xe1(:,k+1)=xe1(:,k)+(G(1)*a(1)*(ze1(:,k)+up1(:)./C(:,2))-2*a(1)*xe1(:,k)-a(1)*a(1)*ye1(:,k))*dt;
    ye1(:,k+1)=ye1(:,k)+xe1(:,k)*dt;
    xs1(:,k+1)=xs1(:,k)+(G(2)*a(2)*zs1(:,k)-2*a(2)*xs1(:,k)-a(2)*a(2)*ys1(:,k))*dt;
    ys1(:,k+1)=ys1(:,k)+xs1(:,k)*dt;
    xl1(:,k+1)=xl1(:,k)+(G(1)*a(1)*uf1(:)-2*a(1)*xl1(:,k)-a(1)*a(1)*yl1(:,k))*dt;
    yl1(:,k+1)=yl1(:,k)+xl1(:,k)*dt;
    xf1(:,k+1)=xf1(:,k)+(G(3)*a(3)*zf1(:,k)-2*a(3)*xf1(:,k)-a(3)*a(3)*yf1(:,k))*dt;
    yf1(:,k+1)=yf1(:,k)+xf1(:,k)*dt; 
end
% Wp_L1L1=buff;
% clear buff