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];
%% LAYER 1
%  Preparazione del layer 1: addestramento e test di autoassociazione con
%  pattern parzialmente corrotto in ingresso. 

%% training layer1 e generazione output per training L2
if train_flag
    L1_train_phase1
    
    Wp_L2L1=eye(numero_colonne)*120; %sinapsi in avanti
    Wp_L1WM=eye(numero_colonne)*100; %sinapsi da WM
    Wp_WML1=eye(numero_colonne)*100; %feedback verso WM
end

%per visualizzare la matrice dei pesi
%figure, imagesc(Wp_L1L1), colormap gray, axis image, colorbar

%% test di autoassociazione:
if test_flag1
    P=randi(size(all_patterns,2),1,1);
    corrupted_input=corrupt_pattern(all_patterns(:,P));
    %prendo un pattern a caso tra quelli caricati e lo corrompo.
    figure
    subplot(121), title('pattern originale'), hold on, axis image
    imagesc(vecToIm(all_patterns(:,P),N,M)), colormap gray
    set(gca, 'YDir','reverse')
    subplot(122), title('pattern corrotto'), hold on, axis image
    imagesc(vecToIm(corrupted_input,N,M)), colormap gray
    set(gca, 'YDir','reverse')

    L1_test
end

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