{
  "abstract": "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.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.13219",
  "author_total": 4,
  "authors": [
    {
      "name": "Yuhang He"
    },
    {
      "name": "Junfeng Zuo"
    },
    {
      "name": "Tianhao Chu"
    },
    {
      "name": "Si Wu"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/22ftf9mw8fzaq.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.13219",
  "fulltext_url": null,
  "id": "22ftf9mw8fzaq",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.13219",
  "language": null,
  "licence": "CC-BY-NC-ND-4.0",
  "licence_label": "CC BY-NC-ND 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.AI"
    ],
    [
      "arxiv",
      "q-bio.NC"
    ],
    [
      "openalex",
      "2805|Memory and Neural Mechanisms"
    ]
  ],
  "openalex": "W7213265855",
  "pdf_url": "https://arxiv.org/pdf/2609.13219",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-01",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/22ftf9mw8fzaq.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:21:30Z",
      "native_id": "2609.13219",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.13219"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "arxiv:q-bio.NC",
      "subfield": "neuroscience"
    }
  ],
  "title": "Planning as Dynamics Relaxation: Hippocampal Recurrent Network Realizes Optimal Goal-Directed Navigation",
  "title_text": "Planning as Dynamics Relaxation: Hippocampal Recurrent Network Realizes Optimal Goal-Directed Navigation",
  "type": "preprint",
  "updated_at": "2026-10-30T12:21:30Z",
  "url": "https://research.draconitic.com/paper/22ftf9mw8fzaq",
  "venue": "arXiv",
  "versions": []
}