Autonomous retrieval for continuous learning in associative memory networks.

The brain's 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 the mammalian central nervous system. The resulting framework provides insights into how neural circuits might maintain memories through purely local interactions and takes a step forward toward a more biologically plausible mechanism for memory rehearsal and continuous learning.