Memory Consolidation and Forgetting During Sleep: A Neural Network Model

摘要

Experimental research with humans and animals suggests that sleep — particularly REM sleep — is, in some way, associated with learning. However, the nature of the association and the underlying mechanism remain unclear. A number of theoretical models have drawn inspiration from research into Artificial Neural Networks. Crick and Mitchinson's ‘unlearning’ and Robins and McCallum's ‘pseudo-rehearsal’ models suggest alternative mechanisms through which sleep could contribute to learning. In this paper we present simulations, suggesting a possible synthesis. Our simulations use a modified version of a Hopfield network to model the possible contribution of sleep to memory consolidation. Sleep is simulated by removing all sensory input to the network and by exposing it to a ‘noise’, intended as a highly abstract model of the signals generated by the Ponto-geniculate-occipital system during sleep. The results show that simulated sleep does indeed contribute to learning and that the relationship between the observed effect and the length of simulated sleep can be represented by a U-shaped curve. It is shown that while high-amplitude, low-frequency noise (reminiscent of NREM sleep) leads to a general reinforcement of memory, low-amplitude, high-frequency noise (as observed in REM sleep) leads to ‘forgetting’ of all but the strongest memory traces. This suggests that a combination of the two kinds of sleep might produce a stronger effect than either kind of sleep on its own and that effective consolidation of memory during sleep may depend not just on REM or NREM sleep but on the overall dynamics of the sleep cycle.