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.

研究提出了一种基于预测编码（PC）的时间预测编码（tPC）模型，它可以通过生物学上可行的神经实现精确地记忆和检索连续的输入，tPC 具有一个隐含的统计白化过程，可以更稳定地处理结构化输入流，并且在多层结构下编码了上下文依赖信息，从而区分出现在序列中的重复元素，这种计算被归功于海马体。