Binding in hippocampal-entorhinal circuits enables compositionality in cognitive maps

TL;DR

This paper introduces a normative model of hippocampal-entorhinal circuits that uses algebraic vector operations to enable robust, scalable, and compositional spatial representations, aligning with biological data and supporting path integration.

Contribution

It presents a novel vector-based algebraic framework for spatial coding in the hippocampus and entorhinal cortex, integrating optimality principles with distributed representations.

Findings

Achieves superlinear scaling of spatial patterns with dimension

Provides robust error correction in spatial representations

Enables hexagonal, carry-free encoding of position

Abstract

We propose a normative model for spatial representation in the hippocampal formation that combines optimality principles, such as maximizing coding range and spatial information per neuron, with an algebraic framework for computing in distributed representation. Spatial position is encoded in a residue number system, with individual residues represented by high-dimensional, complex-valued vectors. These are composed into a single vector representing position by a similarity-preserving, conjunctive vector-binding operation. Self-consistency between the representations of the overall position and of the individual residues is enforced by a modular attractor network whose modules correspond to the grid cell modules in entorhinal cortex. The vector binding operation can also associate different contexts to spatial representations, yielding a model for entorhinal cortex and hippocampus. We show that the model achieves normative desiderata including superlinear scaling of patterns with dimension, robust error correction, and hexagonal, carry-free encoding of spatial position. These properties in turn enable robust path integration and association with sensory inputs. More generally, the model formalizes how compositional computations could occur in the hippocampal formation and leads to testable experimental predictions.