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.
Model Type: Realistic Network
Cell Type(s): Abstract integrate-and-fire leaky neuron
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
Simulation Environment: Brian; Python
Implementer(s): Masquelier, Tim [timothee.masquelier at alum.mit.edu]
References:
Masquelier T, Hugues E, Deco G, Thorpe SJ. (2009). Oscillations, phase-of-firing coding, and spike timing-dependent plasticity: an efficient learning scheme. The Journal of neuroscience : the official journal of the Society for Neuroscience. 29 [PubMed]