ORIGINAL RESEARCH article
Front. Comput. Neurosci.
Volume 19 - 2025 | doi: 10.3389/fncom.2025.1655701
This article is part of the Research TopicUnraveling Information Encoding and Representation in Memory Formation and LearningView all 3 articles
Autonomous Retrieval for Continuous Learning in Associative Memory Networks
Provisionally accepted
- 1Universite Paris-Saclay, Gif-sur-Yvette, France
- 2Laboratoire de Physique des Solides, Orsay, France
- 3CNRS Delegation Paris B, Paris, France
Select one of your emails
You have multiple emails registered with Frontiers:
Notify me on publication
Please enter your email address:
If you already have an account, please login
You don't have a Frontiers account ? You can register here
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 towards a more biologically plausible mechanism for memory rehearsal and continuous learning.
Keywords: neural networks, memory consolidation, continuous learning, Catastrophic Forgetting, unsupervised learning, neuromorphic computing, Associative memory networks
Received: 28 Jun 2025; Accepted: 05 Aug 2025.
Copyright: © 2025 Saighi and Rozenberg. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Paul Saighi, Universite Paris-Saclay, Gif-sur-Yvette, France
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.