STDP allows fast rate-modulated coding with Poisson-like spike trains (Gilson et al. 2011)

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Accession:136717
The model demonstrates that a neuron equipped with STDP robustly detects repeating rate patterns among its afferents, from which the spikes are generated on the fly using inhomogenous Poisson sampling, provided those rates have narrow temporal peaks (10-20ms) - a condition met by many experimental Post-Stimulus Time Histograms (PSTH).
Reference:
1 . Gilson M, Masquelier T, Hugues E (2011) STDP allows fast rate-modulated coding with Poisson-like spike trains. PLoS Comput Biol 7:e1002231 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism:
Cell Type(s): Abstract integrate-and-fire leaky neuron;
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: MATLAB; Brian; Python;
Model Concept(s): Pattern Recognition; Activity Patterns; Coincidence Detection; Spatio-temporal Activity Patterns; Simplified Models; Synaptic Plasticity; Long-term Synaptic Plasticity; Learning; Unsupervised Learning; STDP; Noise Sensitivity; Information transfer;
Implementer(s): Masquelier, Tim [timothee.masquelier at alum.mit.edu];
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GilsonEtAl2011
src
analyze.py
convergence.m
customrefractoriness.py *
generatePeak.m
generateSpikeTrain.m
init.py
main.py
mutualInfo.py
param.m
peak2spike.m
pickleAll.py *
poisson.m
restore.py *
saveCurrent.py
savePot.py
saveWeight.py
spikeToBurst.m
timedLog.m *
timedLogLn.m *
toMatlab.py *
unpickleAll.py *
                            
function timedLogLn(str)
disp(' ');
timedLog(str);

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