Temporal stimulus segmentation by reinforcement learning in populations of spiking neurons

Abstract

Learning to detect, identify or select stimuli is an essential requirement of many behavioral tasks. In real life situations, relevant and non-relevant stimuli are often embedded in a continuous sensory stream, presumably represented by different segments of neural activity. Here, we introduce a neural circuit model that can learn to identify action-relevant stimuli embedded in a spatio-temporal stream of spike trains, while learning to ignore stimuli that are not behaviorally relevant. The model uses a biologically plausible plasticity rule and learns from the reinforcement of correct decisions taken at the right time. Learning is fully online; it is successful for a wide spectrum of stimulus-encoding strategies; it scales well with population size; and can segment cortical spike patterns recorded from behaving animals. Altogether, these results provide a biologically plausible framework of reinforcement learning in the absence of prior information on the identity, relevance and timing of input stimuli.