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

 Download zip file 
Help downloading and running models
"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 (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):
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 after the time dt
%   1. for a given initial potential Vi
%   2. for a membrane with time constant tau_m and resting potential V_rest
%   3. with an external time-dependent current with initial value Ii and time constant decay tau_s

function Vf = Euler_integration_IF_with_external_current( Vi, V_rest, tau_m, Res, Ii, tau_s, dt)

h = 100;        %number of integration steps

dt_h = dt / h;  %corrsponding value of time

Vh = Vi;        %initialization of the membrane potential
Ih = Ii;        %initialization of the external current

i = 1;
while (i <= h)
    Vh = Vh + dt_h * ( ( -(Vh-V_rest) + Res * Ih ) / tau_m );
    Ih = Ih * exp( -dt_h / tau_s);
    i = i + 1;

Vf = Vh;

Loading data, please wait...