Mechanistic Integration of Distal and Proximal Cues in the Rodent Entorhinal-Hippocampal Circuit: Insights From a Biorobotics Model.

Abstract

Rodents depend heavily on visual information to navigate and orient in complex environments, with the entorhinal-hippocampal circuit playing a central role in generating spatial representations that support this behaviour. It is believed that the medial entorhinal cortex (MEC) mainly captures distal visual cues, while the lateral entorhinal cortex (LEC) apprehends most proximal visual cues, both of which cooperatively construct a coordinate system to encode spatial information. However, it remains unclear how entorhinal-hippocampal circuit jointly generate spatial representation from distal and proximal visual cues and further guide navigational decision-making. To fill these gaps, we developed a model based on the two-dimensional continuous attractor network. In the model, allocentric velocity inputs drive grid-cell attractor dynamics anchored to distal cues in the MEC, while LEC populations encode the positions of proximal cues. Their convergence in hippocampal place cells gives rise to a population code of self-location and object location, enabling a simple vector-subtraction mechanism that supports memory-based, goal-directed navigation. To verify the model, we implemented it on a robotics platform. Through systematic biorobotics experiments, the model successfully replicated key findings from biological studies, including distal cue-controlled rotation of grid-place representations, object-related and proximal cue-coherent responses in the LEC pathway and both coherent and irregular remapping patterns at the hippocampal level. Furthermore, it demonstrated a plausible navigation strategy. Overall, these results offer a mechanistic, population-level explanation of how distal and proximal cues can be integrated to maintain stable allocentric representations and support flexible navigation. The biorobotics implementation further demonstrates the value of embodied approaches for testing computational hypotheses of spatial cognition.

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Identifiers

DOI

10.1111/ejn.70344

PubMed

41491595

Journal

The European journal of neuroscience

Year

2026