Sleep as an Offline Consolidation and Recalibration Operator for RS4 Prefiltering

Abstract

Abstract Sleep is strongly implicated in biological memory consolidation, replay, systems-level redistribution of memory, and synaptic renormalization. The present paper does not claim that the RS4 architecture reproduces those biological mechanisms. Instead, it asks a narrower computational question: if sleep is represented as an offline consolidation operator that feeds verified experience back into the next-cycle prefilter, which functional components are necessary, and what trade-offs emerge? Using the same frozen synthetic environment as the companion prefilter study, six offline conditions were compared over 300 confirmatory seeds: no update, replay-only reinforcement, down-selection-only, full replay plus down-selection, an attenuated “fragmented” update at 35% strength, and shuffled feedback. With stable source roles, full and down-selection variants reached perfect reliable-source selection immediately, while replay-only rose from 0.769 after the first offline cycle to 0.997 after the eighth. After an explicit source-role reversal at cycle 4, full feedback fell from 1.000 to 0.667 and recovered to 0.828 by cycle 8; replay-only recovered to 0.836. No-feedback and shuffled controls remained near 0.49. The full-minus-replay final precision difference after reversal was −0.0078 (95% bootstrap CI −0.0222 to 0.0067), indicating no confirmed advantage of the full rule in the changed environment. The benchmark therefore supports two functional conclusions: offline processing can recalibrate a prefilter when feedback is correctly assigned, and aggressive down-selection creates a stability-plasticity cost when source roles change. This is a computational instantiation of a sleep-like Γ consolidation operator, not a biological model of human sleep. Keywords: sleep; memory consolidation; replay; synaptic down-selection; RS4; prefilter; stability-plasticity; offline learning; synthetic benchmark; catastrophic transition