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

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"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. ..."
1 . Esposito U, Giugliano M, Vasilaki E (2014) 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):
Gap Junctions:
Simulation Environment: MATLAB;
Model Concept(s): Synaptic Plasticity; Short-term Synaptic Plasticity; Facilitation; Depression; Learning;
%% This function uses the Euler method to evaluate the membrane potential Vf of N neurons after the time dt and for a given vector of initial potential Vi

function Vf = Euler_integration_conductance_based_IF_multi_synapses( Vi, E, g, tau_g, g_L, dt, N)

h = 100;        %number of integration steps

dt_h = dt / h;  %corrsponding value of time

Vh = Vi;        %initialization of the membrane potential (column vector)
gh = g;         %initialization of the conductances (squared matrix)

i = 1;
while (i <= h)
    Vh = Vh + dt_h * ( -g_L * Vh + diag ( gh * (repmat( E - Vh', N, 1 ) )) ) ;
    gh = gh .* exp( -dt_h / tau_g);
    i = i + 1;    

Vf = Vh;

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