Title: Discrete-attractor-like Tracking in Continuous Attractor Neural Networks,
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.