Sequential activation of place-tuned neurons in an animal during navigation is typically interpreted as reflecting the sequence of input from adjacent positions along the trajectory. More recent theories about such place cells suggest sequences arise from abstract cognitive objectives like planning. Here, we propose a mechanistic and parsimonious interpretation to complement these ideas: hippocampal sequences arise from intrinsic recurrent circuitry that propagates activity without readily available input, acting as a temporal memory buffer for extremely sparse inputs. We implement a minimal sequence generator inspired by neurobiology and pair it with an actor-critic learner for egocentric visual navigation. Our agent reliably solves a continuous maze without explicit geometric cues, with performance depending on the length of the recurrent sequence. Crucially, the model outperforms LSTM cores under sparse input conditions (16 channels, ~2.5% activity), but not under dense input, revealing a strong interaction between representational sparsity and memory architecture. In contrast to LSTM agents, hidden sequence units develop localized place fields, distance-dependent spatial kernels, and task-dependent remapping, while inputs orthogonalize and spatial information increases across layers. These phenomena align with neurobiological data and are causal to performance. Together, our results show that sparse input synergizes with sequence-generating dynamics, providing both a mechanistic account of place cell sequences in the mammalian hippocampus and a simple inductive bias for reinforcement learning based on sparse egocentric inputs in navigation tasks.

翻译：动物导航过程中位置调谐神经元的顺序激活通常被解释为反映了沿轨迹相邻位置输入信号的序列。关于此类位置细胞的较新理论则认为，序列源于抽象认知目标（如路径规划）。本文提出一种机制性且简约的解释作为补充：海马体序列源于内在的循环回路结构，该结构能在缺乏即时输入的情况下传播神经活动，从而为极稀疏的输入提供时间记忆缓冲。我们受神经生物学启发构建了最小序列生成器，并将其与执行者-评判者学习器结合用于自我中心视觉导航。我们的智能体能在缺乏显式几何线索的连续迷宫中可靠完成导航任务，其性能取决于循环序列的长度。关键的是，该模型在稀疏输入条件下（16个通道，约2.5%激活率）优于LSTM核心架构，但在密集输入条件下未显现优势，这揭示了表征稀疏性与记忆架构间的强相互作用。与LSTM智能体相比，隐藏序列单元发展出局部位置场、距离依赖的空间核函数及任务依赖的重映射，而输入信号在层级间正交化且空间信息逐层增强。这些现象与神经生物学数据吻合，且与性能表现存在因果关系。综合而言，我们的研究结果表明：稀疏输入与序列生成动力学具有协同效应，既为哺乳动物海马体位置细胞序列提供了机制性解释，也为基于稀疏自我中心输入的导航任务强化学习提供了简单的归纳偏置。