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Temporal stimulus segmentation by reinforcement learning in populations of spiking neurons

Le Donne, L.; Urbanczik, R.; Senn, W.; La Camera, G.

2020-12-22 neuroscience
10.1101/2020.12.22.424037 bioRxiv
Show 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.

Published in Physical Review E · training set

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