Optimal spatiotemporal spike pattern detection by STDP (Masquelier 2017)

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We simulate a LIF neuron equipped with STDP. A pattern repeats in its inputs. The LIF progressively becomes selective to the repeating pattern, in an optimal manner.
1 . Masquelier T (2018) STDP Allows Close-to-Optimal Spatiotemporal Spike Pattern Detection by Single Coincidence Detector Neurons. Neuroscience 389:133-140 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Neuron or other electrically excitable cell;
Brain Region(s)/Organism:
Cell Type(s): Abstract integrate-and-fire leaky neuron;
Gap Junctions:
Simulation Environment: MATLAB;
Model Concept(s): Coincidence Detection; STDP; Unsupervised Learning; Hebbian plasticity; Long-term Synaptic Plasticity; Pattern Recognition; Spatio-temporal Activity Patterns;
Implementer(s): Masquelier, Tim [timothee.masquelier at alum.mit.edu];
function jittered_pattern = jitter_pattern(pattern,jitter,f,dt)

n = size(pattern,1);
extended_pattern = [ sparse( rand(n,2*jitter/dt)<dt*f ) pattern sparse( rand(n,2*jitter/dt)<dt*f ) ];
m = size(extended_pattern,2);
jittered_pattern = logical(sparse(n,m));

for s = find(extended_pattern)'
    [current_line, current_col] = ind2sub([n,m],s);
    new_col = current_col + round(2*(rand-.5)*jitter/dt);
    if new_col>0 && new_col<=m
%         if jittered_pattern(current_line,new_col)
%             warning('Overwriting a spike')
%         end
        jittered_pattern(current_line,new_col) = true;

jittered_pattern = jittered_pattern(:,jitter/dt+1:end-jitter/dt);

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