Hippocampal indexing alters the stability landscape of synaptic weight space allowing life-long learning
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Systems-level consolidation holds that the hippocampus rapidly encodes new information during wakefulness, and that coordinated cortico-hippocampal replay during subsequent sleep transfers and stabilizes those traces in cortex. This idea captures key learning principles, but exactly how replay reshapes the synaptic-weight landscape - creating new representations while preserving old ones - remains unclear. To address this, we used a biophysically realistic network model to probe the effects of slow-wave sleep (SWS) on synaptic-weight space. We show that previously learned memories are stable attractors in that space, and that hippocampus-driven interactions between sharp-wave ripples and cortical slow waves push the system into new attractor states that jointly encode old and new memories. As a result, replay allows recently acquired information to be incorporated without degrading prior memories. Our results offer a novel mechanistic - and conveniently “geometric” - framework for understanding how sleep-driven replay sculpts synaptic weights during consolidation.
SIGNIFICANCE STATEMENT
Storing, processing, and retrieving information underpins intelligent behavior. Sleep extracts invariant features from prior experience, promoting the emergence of explicit knowledge and insight. Yet despite abundant empirical findings, our understanding of how sleep reshapes memory representations across brain networks remains limited. Here we present a novel framework that describes how memories are encoded in synaptic-weight space and how sleep dynamics reorganize that landscape. These results advance our understanding of how the brain solves core problems of lifelong learning.
