Transitions between spatial attractors in place-cell networks

TL;DR

This paper investigates how neural networks representing spatial maps transition between attractors, revealing two distinct noise-dependent mechanisms supported by simulations and hippocampal data comparisons.

Contribution

It identifies and characterizes two novel transition mechanisms between spatial attractors in place-cell networks, depending on noise levels.

Findings

Two transition scenarios depending on noise: mixed state and weakly localized state.

Numerical simulations confirm the predicted transition mechanisms.

Qualitative comparison with hippocampal place cell recordings during environment changes.

Abstract

The spontaneous transitions between D-dimensional spatial maps in an attractor neural network are studied. Two scenarios for the transition from on map to another are found, depending on the level of noise: (1) through a mixed state, partly localized in both maps around positions where the maps are most similar; (2) through a weakly localized state in one of the two maps, followed by a condensation in the arrival map. Our predictions are confirmed by numerical simulations, and qualitatively compared to recent recordings of hippocampal place cells during quick-environment-changing experiments in rats.