一个神经网络同时生成位置细胞和时间细胞，揭示其共同机制。
When and Where: A Model Hippocampal Network Unifies Formation of Time Cells and Place Cells
- 用单一递归网络模拟海马体CA3，通过预测编码训练
- 空间输入生成稳定位置场，时间输入产生顺序扩展的时序场
- 两种细胞类型可平滑过渡，适合研究记忆与导航的统一模型
海马体的位置细胞和时间细胞分别编码经验的空间与时间特征。两者具有相同的神经基础，但传统模型认为其功能与机制不同：位置细胞为连续吸引子，时间细胞为漏积分器。本文表明，两者均可由单一循环神经网络（RNN）在不同动力学模式下产生。该网络以模拟的、部分遮蔽的“经验向量”为输入，包含空间模式（环境遍历中特定位置的活动）和/或时间模式（间隔“空缺”中的相关活动对），并被训练为重建缺失输入。在空间导航中，网络生成稳定的吸引子类位置场；而在处理时间结构化输入时，产生顺序扩大的场，重现时间细胞特性。通过改变时空输入模式，隐藏单元可平滑过渡于时间细胞与位置细胞表征之间。结果表明，位置细胞与时间细胞有共同起源，但受任务驱动而分化。
原文摘要 · Abstract (English)
Hippocampal place and time cells encode spatial and temporal aspects of experience. Both have the same neural substrate, but have been modeled as having different functions and mechanistic origins, place cells as continuous attractors, and time cells as leaky integrators. Here, we show that both types emerge from two dynamical regimes of a single recurrent network (RNN) modeling hippocampal CA3 as a predictive autoencoder. The network receives simulated, partially occluded ``experience vectors" containing spatial patterns (location-specific activity sampled during environmental traversal) and/or temporal patterns (correlated activity pairs separated by ``void" intervals), and is trained to reconstruct missing input. During spatial navigation, the network generates stable attractor-like place fields. But trained on temporally structured inputs, the network produces sequentially broadened fields, recapitulating time cells. By varying spatio-temporal input patterning, we observe hidden units transition smoothly between time cell-like and place cell-like representations. These results suggest a shared origin, but task-driven difference, between place and time cells.
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