Learning Discrete Successor Transitions in Continuous Attractor Networks: Emergence, Limits, and Topological Constraints

Daniel Brownell

Continuous attractor networks (CANs) are a well-established class of models for representing low-dimensional continuous variables such as head direction, spatial position, and phas…