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Binding in hippocampal-entorhinal circuits enables compositionality in cognitive maps.

Christopher J Kymn¹, Sonia Mazelet^1,2, Anthony Thomas¹

¹Redwood Center for Theoretical Neuroscience, UC Berkeley, Berkeley, USA.

|July 9, 2024

Summary

This study introduces a novel normative model for spatial representation in the hippocampal formation. It uses a residue number system and vector binding for efficient, context-aware spatial coding and path integration.

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Modeling the Functional Network for Spatial Navigation in the Human Brain

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

Area of Science:

Neuroscience
Computational Neuroscience
Cognitive Science

Background:

The hippocampal formation is crucial for spatial memory and navigation.
Existing models often struggle to explain the efficiency and flexibility of spatial representations.

Purpose of the Study:

To propose a normative computational model for spatial representation in the hippocampal formation.
To integrate optimality principles with algebraic computation for distributed representations.
To model context-dependent spatial coding and path integration.

Main Methods:

Developed a normative model using a residue number system for spatial position encoding.
Employed high-dimensional, complex-valued vectors and a conjunctive vector-binding operation.
Utilized a modular attractor network to enforce self-consistency and model grid cell modules.
Integrated context association via the vector binding operation.

Main Results:

The model achieves superlinear scaling, robust error correction, and hexagonal, carry-free spatial encoding.
Demonstrated efficient path integration and association with sensory inputs.
Showcased the model's ability to associate different contexts with spatial representations.

Conclusions:

The proposed model offers a formal framework for compositional computation in the hippocampal formation.
It provides a unified account of spatial representation, context association, and path integration.
The model generates testable predictions for experimental validation.