Discrete-Attractor-like Tracking in Continuous Attractor Neural Networks

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Original languageEnglish
Article number018102
Journal / PublicationPhysical Review Letters
Volume122
Issue number1
Publication statusPublished - 8 Jan 2019
Externally publishedYes

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 the neural state are phase locked with the slow-gamma (∼30-50 Hz) oscillation. Here, we explore the underlying mechanisms of the discontinuous sequence generation. We find 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.

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