Dynamic self-organized error-correction of grid cells by border cells

TL;DR

This paper proposes a biologically plausible, experience-dependent mechanism where border cells correct drift in grid cells during path integration, ensuring robust spatial representation across various environments.

Contribution

It introduces a novel self-organizing error-correction model where border cells learn connectivity to grid cells and reset their phase, improving path integration accuracy.

Findings

Border cells can effectively correct grid cell drift.

The mechanism is robust to environmental shape and complexity.

Predictions for environmental deformation experiments are provided.

Abstract

Grid cells in the entorhinal cortex are believed to establish their regular, spatially correlated firing patterns by path integration of the animal's motion. Mechanisms for path integration, e.g. in attractor network models, predict stochastic drift of grid responses, which is not observed experimentally. We demonstrate a biologically plausible mechanism of dynamic self-organization by which border cells, which fire at environmental boundaries, can correct such drift in grid cells. In our model, experience-dependent Hebbian plasticity during exploration allows border cells to learn connectivity to grid cells. Border cells in this learned network reset the phase of drifting grids. This error-correction mechanism is robust to environmental shape and complexity, including enclosures with interior barriers, and makes distinctive predictions for environmental deformation experiments. Our work demonstrates how diverse cell types in the entorhinal cortex could interact dynamically and adaptively to achieve robust path integration.