Sequential Memory with Temporal Predictive Coding
This work addresses a fundamental neuroscience problem by providing a biologically plausible model for sequential memory, though it is incremental as it extends predictive coding from static to sequential tasks.
The paper tackled the problem of understanding the computational mechanism for sequential memory in the brain by proposing a temporal predictive coding (tPC) model, which accurately memorizes and retrieves sequential inputs with stable performance on structured inputs.
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.