Slow-wave sleep alters the stability landscape of synaptic-weight space allowing life-long learning

Sleep replay - the reactivation of memory traces during slow-wave sleep - is widely held to stabilize memories and reduce interference, yet exactly how replay reorganizes synaptic-weight space to preserve existing memories while incorporating new ones remains unclear. Here, we use a biophysically realistic network model to probe the synaptic-weight dynamics underlying this process. We find that replay drives synaptic weights toward stable configurations - synaptic attractors - that jointly support both old and new memories. Hippocampus-driven interactions between sharp-wave ripples and cortical slow waves guide this reorganization, allowing recently acquired memories to be incorporated without degrading prior ones. These results reveal a mechanistic and geometric framework for memory consolidation: sleep does not passively protect memories, but actively sculpts synaptic-weight space into attractor configurations from which forgetting requires escaping a stability basin. SIGNIFICANCE STATEMENTStoring, 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 at the level of synaptic organization remains limited. Here we present a novel framework that describes how memories are encoded in synaptic-weight space and how sleep dynamics reorganize synaptic landscape. These results advance our understanding of how the brain solves core problems of lifelong learning.