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]; 
% Lo script richiede che siano già costruiti i pattern; si faccia uso della prima sezione
% di MAIN a tal scopo.
clc
close all
clear
MAIN
t_sim=1.5;
dt=0.0001;
t=0:dt:t_sim;

train_flag=0; 

M_fac = 1.7*2/3;  % multiplicative factor, representing attention
%% five patterns
load_sinapsi
Wp_WML1=Wp_WML1*3; %feedback L1->WM maggiorato
Wp_L1WM=Wp_L1WM*3; %propagazione in avanti WM->L1 maggiorata
INPUT_WM=zeros(numero_colonne,length(t));
pos=find(corrupt_pattern(sum(all_patterns(:,1:5),2))==1);
nP=5;
INPUT_WM(pos,1:499)=1;

if nP>4
    A_L2L2=A_L2L2*M_fac;
    A_L3L3=A_L3L3*M_fac;
%     A_L2L2=A_L2L2*(1.025+0.025*(nP-3)); %da +2.5% a +15%
%     A_L3L3=A_L3L3*(1.025+0.025*(nP-3));
else
    A_L2L2=A_L2L2*2/3;
    A_L3L3=A_L3L3*2/3;
end

reteWM_desync

fonte = 12;
linea = 1;
figure


subplot(511), hold on, ylabel('zp L3')
plot(t,sum(zp3(pos1,:))/size(pos1,2),'b','linewidth',linea)
plot(t,sum(zp3(pos2,:))/size(pos2,2),'r','linewidth',linea)
plot(t,sum(zp3(pos3,:))/size(pos3,2),'g','linewidth',linea)
plot(t,sum(zp3(pos4,:))/size(pos4,2),'k','linewidth',linea)
plot(t,sum(zp3(pos5,:))/size(pos5,2),'c','linewidth',linea)
plot(t,sum(zp3(pos6,:))/size(pos6,2),'b--','linewidth',linea)
plot(t,sum(zp3(pos7,:))/size(pos7,2),'r--','linewidth',linea)
plot(t,sum(zp3(pos8,:))/size(pos8,2),'g--','linewidth',linea)
plot(t,sum(zp3(pos9,:))/size(pos9,2),'k--','linewidth',linea)
set(gca,'fontsize',fonte)

if SET_PATT==3
 plot(t,sum(zp3(pos10,:))/size(pos10,2),'c--','linewidth',linea)
end

%% six patterns
load_sinapsi
Wp_WML1=Wp_WML1*3; %feedback L1->WM maggiorato
Wp_L1WM=Wp_L1WM*3; %propagazione in avanti WM->L1 maggiorata
INPUT_WM=zeros(numero_colonne,length(t));
pos=find(corrupt_pattern(sum(all_patterns(:,1:6),2))==1);
nP=6;
INPUT_WM(pos,1:499)=1;

if nP>4
    A_L2L2=A_L2L2*M_fac;
    A_L3L3=A_L3L3*M_fac;
%     A_L2L2=A_L2L2*(1.025+0.025*(nP-3)); %da +2.5% a +15%
%     A_L3L3=A_L3L3*(1.025+0.025*(nP-3));
else
    A_L2L2=A_L2L2*2/3;
    A_L3L3=A_L3L3*2/3;
end

reteWM_desync

subplot(512), hold on, ylabel('zp L3') 
plot(t,sum(zp3(pos1,:))/size(pos1,2),'b','linewidth',linea)
plot(t,sum(zp3(pos2,:))/size(pos2,2),'r','linewidth',linea)
plot(t,sum(zp3(pos3,:))/size(pos3,2),'g','linewidth',linea)
plot(t,sum(zp3(pos4,:))/size(pos4,2),'k','linewidth',linea)
plot(t,sum(zp3(pos5,:))/size(pos5,2),'c','linewidth',linea)
plot(t,sum(zp3(pos6,:))/size(pos6,2),'b--','linewidth',linea)
plot(t,sum(zp3(pos7,:))/size(pos7,2),'r--','linewidth',linea)
plot(t,sum(zp3(pos8,:))/size(pos8,2),'g--','linewidth',linea)
plot(t,sum(zp3(pos9,:))/size(pos9,2),'k--','linewidth',linea)
set(gca,'fontsize',fonte)

if SET_PATT==3
 plot(t,sum(zp3(pos10,:))/size(pos10,2),'c--','linewidth',linea)
end

%% seven patterns
load_sinapsi
Wp_WML1=Wp_WML1*3; %feedback L1->WM maggiorato
Wp_L1WM=Wp_L1WM*3; %propagazione in avanti WM->L1 maggiorata
INPUT_WM=zeros(numero_colonne,length(t));
pos=find(corrupt_pattern(sum(all_patterns(:,1:7),2))==1);
nP=7;
INPUT_WM(pos,1:499)=1;

if nP>4
    A_L2L2=A_L2L2*M_fac;
    A_L3L3=A_L3L3*M_fac;
%     A_L2L2=A_L2L2*(1.025+0.025*(nP-3)); %da +2.5% a +15%
%     A_L3L3=A_L3L3*(1.025+0.025*(nP-3));
else
    A_L2L2=A_L2L2*2/3;
    A_L3L3=A_L3L3*2/3;
end

reteWM_desync

subplot(513), hold on, ylabel('zp L3') 
plot(t,sum(zp3(pos1,:))/size(pos1,2),'b','linewidth',linea)
plot(t,sum(zp3(pos2,:))/size(pos2,2),'r','linewidth',linea)
plot(t,sum(zp3(pos3,:))/size(pos3,2),'g','linewidth',linea)
plot(t,sum(zp3(pos4,:))/size(pos4,2),'k','linewidth',linea)
plot(t,sum(zp3(pos5,:))/size(pos5,2),'c','linewidth',linea)
plot(t,sum(zp3(pos6,:))/size(pos6,2),'b--','linewidth',linea)
plot(t,sum(zp3(pos7,:))/size(pos7,2),'r--','linewidth',linea)
plot(t,sum(zp3(pos8,:))/size(pos8,2),'g--','linewidth',linea)
plot(t,sum(zp3(pos9,:))/size(pos9,2),'k--','linewidth',linea)
set(gca,'fontsize',fonte)

if SET_PATT==3
 plot(t,sum(zp3(pos10,:))/size(pos10,2),'c--','linewidth',linea)
end

%% eight patterns
load_sinapsi
Wp_WML1=Wp_WML1*3; %feedback L1->WM maggiorato
Wp_L1WM=Wp_L1WM*3; %propagazione in avanti WM->L1 maggiorata
INPUT_WM=zeros(numero_colonne,length(t));
pos=find(corrupt_pattern(sum(all_patterns(:,1:8),2))==1);
nP=8;
INPUT_WM(pos,1:499)=1;

if nP>4
    A_L2L2=A_L2L2*M_fac;
    A_L3L3=A_L3L3*M_fac;
%     A_L2L2=A_L2L2*(1.025+0.025*(nP-3)); %da +2.5% a +15%
%     A_L3L3=A_L3L3*(1.025+0.025*(nP-3));
else
    A_L2L2=A_L2L2*2/3;
    A_L3L3=A_L3L3*2/3;
end

reteWM_desync

subplot(514), hold on, ylabel('zp L3') 
plot(t,sum(zp3(pos1,:))/size(pos1,2),'b','linewidth',linea)
plot(t,sum(zp3(pos2,:))/size(pos2,2),'r','linewidth',linea)
plot(t,sum(zp3(pos3,:))/size(pos3,2),'g','linewidth',linea)
plot(t,sum(zp3(pos4,:))/size(pos4,2),'k','linewidth',linea)
plot(t,sum(zp3(pos5,:))/size(pos5,2),'c','linewidth',linea)
plot(t,sum(zp3(pos6,:))/size(pos6,2),'b--','linewidth',linea)
plot(t,sum(zp3(pos7,:))/size(pos7,2),'r--','linewidth',linea)
plot(t,sum(zp3(pos8,:))/size(pos8,2),'g--','linewidth',linea)
plot(t,sum(zp3(pos9,:))/size(pos9,2),'k--','linewidth',linea)
set(gca,'fontsize',fonte)

if SET_PATT==3
 plot(t,sum(zp3(pos10,:))/size(pos10,2),'c--','linewidth',linea)
end

%% nine patterns
load_sinapsi
Wp_WML1=Wp_WML1*3; %feedback L1->WM maggiorato
Wp_L1WM=Wp_L1WM*3; %propagazione in avanti WM->L1 maggiorata
INPUT_WM=zeros(numero_colonne,length(t));
pos=find(corrupt_pattern(sum(all_patterns(:,1:9),2))==1);
nP=9;
INPUT_WM(pos,1:499)=1;

if nP>4
    A_L2L2=A_L2L2*M_fac;
    A_L3L3=A_L3L3*M_fac;
%     A_L2L2=A_L2L2*(1.025+0.025*(nP-3)); %da +2.5% a +15%
%     A_L3L3=A_L3L3*(1.025+0.025*(nP-3));
else
    A_L2L2=A_L2L2*2/3;
    A_L3L3=A_L3L3*2/3;
end

reteWM_desync

subplot(515), hold on, ylabel('zp L3') 
plot(t,sum(zp3(pos1,:))/size(pos1,2),'b','linewidth',linea)
plot(t,sum(zp3(pos2,:))/size(pos2,2),'r','linewidth',linea)
plot(t,sum(zp3(pos3,:))/size(pos3,2),'g','linewidth',linea)
plot(t,sum(zp3(pos4,:))/size(pos4,2),'k','linewidth',linea)
plot(t,sum(zp3(pos5,:))/size(pos5,2),'c','linewidth',linea)
plot(t,sum(zp3(pos6,:))/size(pos6,2),'b--','linewidth',linea)
plot(t,sum(zp3(pos7,:))/size(pos7,2),'r--','linewidth',linea)
plot(t,sum(zp3(pos8,:))/size(pos8,2),'g--','linewidth',linea)
plot(t,sum(zp3(pos9,:))/size(pos9,2),'k--','linewidth',linea)
xlabel('time (s)')
set(gca,'fontsize',fonte)

if SET_PATT==3
 plot(t,sum(zp3(pos10,:))/size(pos10,2),'c--','linewidth',linea)
end
legend('obj1','obj2','obj3','obj4','obj5','obj6','obj7', 'obj8','obj9')