Memorizing the temporal order of event sequences is critical for the survival 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 static memory tasks, in this work we propose a novel PC-based model for sequential memory, called 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 with a multi-layer structure can encode context-dependent information, thus distinguishing between repeating elements appearing in a sequence, a computation attributed to the hippocampus. Our work establishes a possible computational mechanism underlying sequential memory in the brain that can also be theoretically interpreted using existing memory model frameworks.

翻译：事件序列的时间顺序记忆对生物体的生存至关重要。然而，大脑中序列记忆的计算机制尚不明确。受神经科学理论启发，并借鉴预测编码（PC）在静态记忆任务中的成功应用，本文提出了一种基于PC的序列记忆新模型——时序预测编码（tPC）。研究表明，我们的tPC模型能够通过符合生物学原理的神经实现，精确地记忆和检索时序输入。重要的是，我们的理论分析揭示，tPC可被视作一种带有隐式统计白化过程的经典非对称Hopfield网络（AHN），从而在处理结构化输入的序列记忆任务中表现出更稳定的性能。此外，我们发现具有多层结构的tPC能够编码上下文依赖信息，从而区分序列中出现的重复元素——这一计算能力通常归因于海马体。本研究为大脑中的序列记忆建立了可能的计算机制，该机制也可借助现有记忆模型框架进行理论解释。