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];
function [corrupted_input]=corrupt_pattern(pattern)
% corrompe un pattern selezionato, ponendo il 30% dei suoi pixel a 0.

index=find(pattern==1);
corrupted_input=pattern;
Conta=sum(pattern); %numero di pixel a 1 pre-corruzione.
rng('shuffle')
while sum(corrupted_input)>0.7*Conta
    n=randi(length(index),1,1);
    pos=index(n);
    corrupted_input(pos)=0;
end

end


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