Sequential memory with temporal predictive coding

Conference item

Forming accurate memory of sequential stimuli is a fundamental function of biological agents. However, the computational mechanism underlying sequential memory in the brain remains unclear. Inspired by neuroscience theories and recent successes in applying predictive coding (PC) to \emph{static} memory tasks, in this work we propose a novel PC-based model for \emph{sequential} memory, called \emph{temporal predictive coding} (tPC). We show that our tPC models can memorize and retrieve sequential inputs accurately with a biologically plausible neural implementation. Importantly, our analytical study reveals that tPC can be viewed as a classical Asymmetric Hopfield Network (AHN) with an implicit statistical whitening process, which leads to more stable performance in sequential memory tasks of structured inputs. Moreover, we find that tPC exhibits properties consistent with behavioral observations and theories in neuroscience, thereby strengthening its biological relevance. Our work establishes a possible computational mechanism underlying sequential memory in the brain that can also be theoretically interpreted using existing memory model frameworks.

Authors: Bogacz, R; Barron, H; Tang, M

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Curran Associates
• Host title: Advances in Neural Information Processing Systems 36 (NeurIPS 2023)
• Pages: 44341-44355
• Publication date: 2024-07-01
• Acceptance date: 2024-03-02
• Event title: 37th Conference on Neural Information Processing Systems (NeurIPS 2023)
• Event location: New Orleans, LA, USA
• Event website: https://neurips.cc/Conferences/2023
• Event start date: 2023-12-10
• Event end date: 2023-12-16
• DOI: 10.52202/075280-1919
• ISSN: 1049-5258
• EISBN: 9781713899921
• Language: English