Discrete-attractor-like Tracking in Continuous Attractor Neural Networks

TL;DR

This paper investigates how continuous attractor neural networks can produce discrete, phase-locked state transitions, explaining hippocampal neuron replay phenomena without assuming inherent discreteness.

Contribution

It demonstrates that discrete-attractor-like behavior can naturally emerge in continuous attractor neural networks through specific interaction phases.

Findings

Discrete transitions are phase-locked with brain rhythms.

Emergence of discrete behavior without explicit discreteness assumptions.

Multiple phases depending on input and inhibitory feedback.

Abstract

Continuous attractor neural networks generate a set of smoothly connected attractor states. In memory systems of the brain, these attractor states may represent continuous pieces of information such as spatial locations and head directions of animals. However, during the replay of previous experiences, hippocampal neurons show a discontinuous sequence in which discrete transitions of neural state are phase-locked with the slow-gamma (30-40 Hz) oscillation. Here, we explored the underlying mechanisms of the discontinuous sequence generation. We found that a continuous attractor neural network has several phases depending on the interactions between external input and local inhibitory feedback. The discrete-attractor-like behavior naturally emerges in one of these phases without any discreteness assumption. We propose that the dynamics of continuous attractor neural networks is the key to generate discontinuous state changes phase-locked to the brain rhythm.