Selective Hippocampal Reactivation Driven by Neocortical Feedback Supports Memory Transfer in Predictive-Coding Networks
Abstract
During sleep and quiet wakefulness, the brain spontaneously revisits neural activity patterns associated with previous experience, a phenomenon known as reactivation. Reactivation is considered to support systems consolidation, the process by which memory retrieval becomes progressively more dependent on neocortical circuits and less dependent on the hippocampus. Evidence indicates that reactivation is not indiscriminate but preferentially targets specific memory traces. Systems consolidation has motivated extensive computational modelling of spontaneous reactivation, the prioritisation of specific memories for replay, and the redistribution of memory representations between the hippocampus and neocortex. In parallel, the development of predictive-coding (PC) models has shown how computations attributed to hippocampal and neocortical circuits can arise from local prediction-error dynamics. Here, we connect these two lines of work by showing how autonomous intrinsic dynamics in coupled PC networks can transfer stored memory content between neural systems. The hippocampus and neocortex are modelled as two coupled PC associative memory networks. The architecture incorporates asymmetric coupling motivated by reported interactions between hippocampal area CA1 and the prefrontal cortex. This coupling establishes a closed-loop dialogue in which hippocampal activity trains the neocortical network, while neocortical feedback biases subsequent hippocampal activity toward content for…
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