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Learning may need only a few bits of synaptic precision
Physical review. E
Learning in neural networks poses peculiar challenges when using discretized rather then continuous synaptic states. The choice of discrete synapses is motivated by biological reasoning and experiments, and possibly by hardware implementation considerations as well. In this paper we extend a previous large deviations analysis which unveiled the existence of peculiar dense regions in the space of synaptic states which accounts for the possibility of learning efficiently in networks with binarydoi:10.1103/physreve.93.052313 pmid:27300916 fatcat:74fcr7zamnh3diomwspou6dxji