Efficient low-dimensional approximation of continuous attractor networks

Abstract

Continuous "bump" attractors are an established model of cortical working memory for continuous variables and can be implemented using various neuron and network models. Here, we develop a generalizable approach for the approximation of bump states of continuous attractor networks implemented in networks of both rate-based and spiking neurons. The method relies on a low-dimensional parametrization of the spatial shape of firing rates, allowing to apply efficient numerical optimization methods. Using our theory, we can establish a mapping between network structure and attractor properties that allows the prediction of the effects of network parameters on the steady state firing rate profile and the existence of bumps, and vice-versa, to fine-tune a network to produce bumps of a given shape.