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Poster Session A: Tuesday, August 12, 1:30 – 4:30 pm, de Brug & E‑Hall

An Entorhinal-hippocampal Systems Model for Spatial Navigation and Memory

Tianhao Chu¹, Zilong Ji², Neil Burgess³, Si Wu¹; ¹Peking University, ²University College London, University of London, ³University College London

Presenter: Zilong Ji

Simultaneously localizing one's position and constructing a map of the surrounding environment are fundamental processes underpinning model-based navigation, reasoning, and decision-making. The brain achieves this ability through two complementary strategies: inferring one’s states in the environment from sensory inputs and updating previous states through path integration. However, how these two sources of information interact remains largely unknown. Here, we introduce EHSLAM, a mechanistic computational framework of the entorhinal-hippocampal system. By integrating sensory inputs and path-integrative signals, EHSLAM learns localized representations of space as place cells in the hippocampus, by updating synaptic connections in the network after encountering a novel environment. These phenomena are interrelated and mutually reinforce during spatial updates in this framework. Furthermore, EHSLAM captures key findings from empirical data, including place cell remapping and grid cell realignment across distinct environments, as well as making testable predictions. This computational framework provides a mechanistic understanding of the neural dynamics involved in spatial navigation and memory.

Topic Area: Memory, Spatial Cognition & Skill Learning

Extended Abstract: Full Text PDF