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).
Model Type: Realistic Network
Cell Type(s): Abstract integrate-and-fire leaky neuron
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
Simulation Environment: MATLAB; Brian; Python
Implementer(s): Masquelier, Tim [timothee.masquelier at alum.mit.edu]
References:
Gilson M, Masquelier T, Hugues E. (2011). STDP allows fast rate-modulated coding with Poisson-like spike trains. PLoS computational biology. 7 [PubMed]