Oscillations, phase-of-firing coding and STDP: an efficient learning scheme (Masquelier et al. 2009)

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Accession:123928
The model demonstrates how a common oscillatory drive for a group of neurons formats and reliabilizes their spike times - through an activation-to-phase conversion - so that repeating activation patterns can be easily detected and learned by a downstream neuron equipped with STDP, and then recognized in just one oscillation cycle.
Reference:
1 . Masquelier T, Hugues E, Deco G, Thorpe SJ (2009) Oscillations, phase-of-firing coding, and spike timing-dependent plasticity: an efficient learning scheme. J Neurosci 29:13484-93 [PubMed]
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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: Brian; Python;
Model Concept(s): Pattern Recognition; Activity Patterns; Coincidence Detection; Temporal Pattern Generation; Oscillations; Synchronization; Spatio-temporal Activity Patterns; Synaptic Plasticity; Long-term Synaptic Plasticity; Unsupervised Learning; STDP;
Implementer(s): Masquelier, Tim [timothee.masquelier at alum.mit.edu]; 
# save weights
outputdata={'weight':finalWeight}
savemat('../data/weight.'+'%03d' % (randState)+'.mat',outputdata)
t=localtime()
savemat('../data/weight.'+'%03d' % (randState)+ '.%02d' % t[1] + '.%02d' % t[2] + '.%02d' % t[3] + '.%02d' % t[4] +'.mat',outputdata)
del outputdata