规划作为动力学松弛：海马递归网络实现最优目标导向导航
Planning as Dynamics Relaxation: Hippocampal Recurrent Network Realizes Optimal Goal-Directed Navigation
另 2 家 · 查看机构详情
- Peking University（北京大学）
- IDG/McGovern Institute for Brain Research（IDG/麦戈文脑科学研究所）
- Peking-Tsinghua Center for Life Sciences（北大-清华生命科学中心）
- Academy for Advanced Interdisciplinary Studies, Peking University（北京大学前沿交叉学科研究院）
- Center of Quantitative Biology, Peking University（北京大学定量生物学中心）
机构由 AI 辅助整理，请以论文原文为准。
浏览论文内容
中文总结 AI 辅助
本研究提出海马递归网络通过松弛动力学实现最优目标导向导航，其活动场等价于LMDP期望场，理论分析与模拟证明其在复杂障碍环境中高效鲁棒，且支持局部环境变化的低秩更新。
中文摘要 AI 辅助
空间认知地图的神经相关性已被充分记录，但神经回路如何在复杂环境中执行空间导航——例如在避开障碍物的同时到达目标——仍 largely 不清楚。在此，我们表明，具有适当递归连接的海马网络可以通过其松弛动力学自然地实现最优目标导向导航。具体而言，我们考虑神经元之间的递归权重表示由神经元编码的空间位置之间的转移概率；因此，墙壁和阻塞走廊等障碍物反映为连接权重的消失。这种连接模式可以在动物探索环境时通过行为时间尺度突触可塑性（BTSP）在海马中学习。当呈现目标信号时，网络动力学将松弛到代表目标位置的活动场。我们证明该场在数学上等价于线性可解马尔可夫决策过程（LMDP）的期望场，并且该场的局部对数梯度指示导航方向。理论分析和模拟均表明，这种递归网络动力学介导的导航在具有复杂障碍物布局的环境中高效且鲁棒。此外，当环境发生局部变化时，仅需对网络的连接模式进行低秩更新。我们希望这项研究为大脑中超越空间导航的抽象理性地图中的规划提供一般回路原理的见解。
英文摘要
Neural correlates of spatial cognitive map are well documented, yet exactly how neural circuits perform spatial navigation in complex environments - e.g., reaching a goal while avoiding obstacles - remains largely unclear. Here, we show that a hippocampal network with appropriate recurrent connections can naturally achieve optimal goal-directed navigation via its relaxation dynamics. Specifically, we consider that the recurrent weights between the neurons represent the transition probabilities between spatial locations encoded by neurons; obstacles such as walls and blocked corridors are therefore reflected by the vanishing of connection weights. This connection pattern can be learned in the hippocampus via behavioral-timescale synaptic plasticity (BTSP) while the animal is exploring the environment. When a goal signal is presented, the network dynamics will relax into an activity field representing the goal location. We prove that this field is mathematically equivalent to the desirability field of a Linearly-solvable Markov Decision Process (LMDP), and the local log-gradient of the field indicates the navigation direction. Both theoretical analyses and simulations demonstrate that this recurrent network dynamics-mediated navigation is efficient and robust in environments with complex obstacle layouts. Moreover, only low-rank updates of the network's connection pattern are needed when the environment has local changes. We hope this study offers insight into a general circuit principle for planning in abstract rational maps in the brain beyond spatial navigation.
