← 研踪 PaperTrailarXiv:2609.13219 · v2
Planning as Dynamics Relaxation: Hippocampal Recurrent Network Realizes Optimal Goal-Directed Navigation
首次提交 2026-08-31 00:48
版本更新 2026-11-12 09:05
雷达观测 2026-11-13 14:16
摘要
Neural correlates of the cognitive map are well documented, yet how a neural circuit turns a stored map into an obstacle-avoiding route remains unclear. Here we show that a hippocampal network with appropriate recurrent connections achieves optimal goal-directed navigation through its own relaxation dynamics. The recurrent weights represent the transition probabilities between the locations the neurons encode, walls are reflected by vanishing weights, and behavioral-timescale synaptic plasticity (BTSP) writes this connectivity during goal-free exploration. When a goal is recalled, a top-down current clamps the goal neuron and the population relaxes into a goal-locked activity field. We prove that this field is the desirability field of a linearly-solvable Markov decision process: the relaxed state is the exact optimum of a stated control problem, the KL-regularized objective of Todorov, and the optimal next-step distribution is carried by the branch synaptic currents. The identity extends to directed transitions, and a weak-cost limit returns the Green's function of the recurrent Laplacian. A place-tuned readout converts the field's log-gradient into a direction of travel, and the same synapses answer a predictive query when the source is placed at the current location. Simulations on mazes with complex obstacles show near-shortest routes, an identity that survives a thousand-fold range of recall amplitude, the loss of 80% of place cells, and tens of thousands of states, low-rank updates of every plan after a local change, and a transient that is anytime: the local action is ready long before the field converges. We hope this offers a general circuit principle for planning in abstract cognitive maps beyond spatial navigation.
作者备注：Accepted at NeurIPS 2026 (Spotlight). Camera-ready version. 63 pages, including appendices and checklist
观测到的版本
相邻记录可能跳过中间版本；首次发现 v2 及以上也会标为版本更新。
v2 · 2026-11-12 09:05 · 最新观测版本
Planning as Dynamics Relaxation: Hippocampal Recurrent Network Realizes Optimal Goal-Directed Navigation
Neural correlates of the cognitive map are well documented, yet how a neural circuit turns a stored map into an obstacle-avoiding route remains unclear. Here we show that a hippocampal network with appropriate recurrent connections achieves optimal goal-directed navigation through its own relaxation dynamics. The recurrent weights represent the transition probabilities between the locations the neurons encode, walls are reflected by vanishing weights, and behavioral-timescale synaptic plasticity (BTSP) writes this connectivity during goal-free exploration. When a goal is recalled, a top-down current clamps the goal neuron and the population relaxes into a goal-locked activity field. We prove that this field is the desirability field of a linearly-solvable Markov decision process: the relaxed state is the exact optimum of a stated control problem, the KL-regularized objective of Todorov, and the optimal next-step distribution is carried by the branch synaptic currents. The identity extends to directed transitions, and a weak-cost limit returns the Green's function of the recurrent Laplacian. A place-tuned readout converts the field's log-gradient into a direction of travel, and the same synapses answer a predictive query when the source is placed at the current location. Simulations on mazes with complex obstacles show near-shortest routes, an identity that survives a thousand-fold range of recall amplitude, the loss of 80% of place cells, and tens of thousands of states, low-rank updates of every plan after a local change, and a transient that is anytime: the local action is ready long before the field converges. We hope this offers a general circuit principle for planning in abstract cognitive maps beyond spatial navigation.
查看此版本解读 →查看此版本原文 ↗