Spike-Timing-Dependent Synaptic Plasticity to Learn Spatiotemporal Patterns in Recurrent Neural Networks

Abstract

Assuming asymmetric time window of the spike-timing- dependent synaptic plasticity (STDP), we study spatiotemporal learning in recurrent neural networks. We first show numerical simulations of spiking neural networks in which spatiotemporal Poisson patterns (i.e., random spatiotemporal patterns generated by independent Poisson process) are successfully memorized by the STDP-based learning rule. Then, we discuss the underlying mechanism of the STDP-based learning, mentioning our recent analysis on associative memory analog neural networks for periodic spatiotemporal patterns. Order parameter dynamics in the analog neural networks explains time scale change in retrieval process and the shape of the STDP time window optimal to encode a large number of spatiotemporal patterns. The analysis further elucidates phase transition due to destabilization of retrieval state. These findings on analog neural networks are found to be consistent with the previous results on spiking neural networks. These STDP-based spatiotemporal associative memory possibly gives some insights into the recent experimental results in which spatiotemporal patterns are found to be retrieved at the various time scale.