ROBUST TIMING AND MOTOR PATTERNS BY TAMING CHAOS IN RECURRENT NEURAL NETWORKS

Abstract

The brain’s ability to tell time and produce complex spatiotemporal motor patterns is critical to anticipating the next ring of a telephone or playing a musical instrument. One class of models proposes that these abilities emerge from dynamically changing patterns of neural activity generated within recurrent neural networks. However, the relevant dynamic regimes of recurrent networks are highly sensitive to noise, i.e., chaotic. We describe a firing rate model that tells time on the order of seconds and generates complex spatiotemporal patterns in the presence of high levels of noise. This is achieved through the tuning of the recurrent connections. The network operates in a novel dynamic regime that exhibits coexisting chaotic and locally stable trajectories. These stable patterns function as “dynamic attractors” and provide a novel feature characteristic of biological systems: the ability to “return” to the pattern being generated in the face of perturbations.