Complexity without chaos: Plasticity within random recurrent networks generates robust timing and motor control

TL;DR

This paper demonstrates how random recurrent neural networks can be tuned to produce complex, stable, and noise-robust neural activity patterns, enhancing their utility for timing and motor control tasks.

Contribution

It introduces a supervised learning method that stabilizes chaotic dynamics in RRNs, enabling robust complex activity patterns for motor control.

Findings

RRNs can be tuned to produce stable, complex activity patterns

The learning rule enhances the generation of spatiotemporal motor patterns

Neural variability decreases in response to stimulus onset

Abstract

It is widely accepted that the complex dynamics characteristic of recurrent neural circuits contributes in a fundamental manner to brain function. Progress has been slow in understanding and exploiting the computational power of recurrent dynamics for two main reasons: nonlinear recurrent networks often exhibit chaotic behavior and most known learning rules do not work in robust fashion in recurrent networks. Here we address both these problems by demonstrating how random recurrent networks (RRN) that initially exhibit chaotic dynamics can be tuned through a supervised learning rule to generate locally stable neural patterns of activity that are both complex and robust to noise. The outcome is a novel neural network regime that exhibits both transiently stable and chaotic trajectories. We further show that the recurrent learning rule dramatically increases the ability of RRNs to generate complex spatiotemporal motor patterns, and accounts for recent experimental data showing a decrease in neural variability in response to stimulus onset.