Adaptation of Short-Term Plasticity parameters (Esposito et al. 2015)

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Accession:169242
"The anatomical connectivity among neurons has been experimentally found to be largely non-random across brain areas. This means that certain connectivity motifs occur at a higher frequency than would be expected by chance. Of particular interest, short-term synaptic plasticity properties were found to colocalize with specific motifs: an over-expression of bidirectional motifs has been found in neuronal pairs where short-term facilitation dominates synaptic transmission among the neurons, whereas an over-expression of unidirectional motifs has been observed in neuronal pairs where short-term depression dominates. In previous work we found that, given a network with fixed short-term properties, the interaction between short- and long-term plasticity of synaptic transmission is sufficient for the emergence of specific motifs. Here, we introduce an error-driven learning mechanism for short-term plasticity that may explain how such observed correspondences develop from randomly initialized dynamic synapses. ..."
Reference:
1 . Esposito U, Giugliano M, Vasilaki E (2015) Adaptation of short-term plasticity parameters via error-driven learning may explain the correlation between activity-dependent synaptic properties, connectivity motifs and target specificity. Front Comput Neurosci 8:175 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Synapse;
Brain Region(s)/Organism:
Cell Type(s):
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: MATLAB;
Model Concept(s): Synaptic Plasticity; Short-term Synaptic Plasticity; Facilitation; Depression; Learning;
Implementer(s):
%% Numerical simulation for the symmetry measure. Uniform distribution

function [s] = sym_measure (matrix)
        
    upper = triu(matrix,1);         %extract the upper triangle matrix
    lower = tril(matrix,-1)';       %extract the lower triangle matrix and transpose it

    x = upper(:);                   %convert the matrix into a vector
    y = lower(:);                   %convert the matrix into a vector

    temp = x + y;                   %sum vector elements==sum the reciprocal elements of the matrix
    nonzero_index = find(temp~=0.); %create a vector whoose elements are the index of the non zero elements in temp
    K = length(nonzero_index);      %counts how many elements of temp are nonzero==counts the number of pairs connections for which at least one direction is nonzero

    if K > 0
        s = 1 - sum ( abs(x(nonzero_index)-y(nonzero_index)) ./ (x(nonzero_index)+y(nonzero_index)) ) / K;
    else
        s = 0;
    end

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