Autonomous Retrieval for Continuous Learning in Associative Memory Networks

Abstract

The brains faculty to assimilate and retain information, continually updating its memory while limiting the loss of valuable past knowledge, remains largely a mystery. We address this challenge related to continuous learning in the context of associative memory networks, where the sequential storage of correlated patterns typically requires non-local learning rules or external memory systems. Our work demonstrates how incorporating biologically-inspired inhibitory plasticity enables networks to autonomously explore their attractor landscape. The algorithm presented here allows for the autonomous retrieval of stored patterns, enabling the progressive incorporation of correlated memories. This mechanism is reminiscent of memory consolidation during sleep-like states in biological systems. The resulting framework provides insights into how neural circuits might maintain memories through purely local interactions, and takes a step forward towards a more biologically plausible mechanism for continuous learning. Author summaryCatastrophic forgetting - when acquiring new knowledge seriously degrades previously learned information - remains a fundamental challenge in machine learning, affecting systems from simple associative networks to complex language models. One widely studied approach to mitigate forgetting is memory rehearsal, in which prior stored information are periodically replayed, an idea supported by neurophysiological evidence of memory consolidation during sleep. In this work, we show that networks with plastic inhibitory connections can spontaneously recall stored memories without external guidance. This built-in retrieval mechanism allows for the incorporation of new memories while preserving older ones, opening a path toward a biologically inspired solution to the problem of catastrophic forgetting.