Task-specific programming of chaos in neural circuits

TL;DR

This paper demonstrates how tuning network topology in neural circuits can control chaos for task-specific reservoir computing, enabling reconfigurable and low-latency computation.

Contribution

It introduces a method to program chaos in neural circuits by adjusting network topology, expanding the design space beyond element-level parameters.

Findings

Tuning network topology induces an ordered-to-chaotic transition.

Small-world connectivity enables low-latency chaos switching.

Network topology serves as a reconfigurable parameter for computation.

Abstract

Chaotic dynamics have emerged as a versatile resource for neuromorphic and probabilistic computing, enabling high-dimensional nonlinear processing and classical analogues of quantum randomness. Exploiting chaos for computation requires task-dependent control over complexity, as demonstrated in reservoir computing, random-number generation, and probabilistic inference. Existing approaches have focused on tuning element-level parameters, leaving the collective, many-body origin of chaos largely unexplored as a design freedom. Here, we demonstrate programmable chaotic dynamics for task-specific reservoir computing. Using a continuous-time neural-circuit model, we show that tuning network topology drives an ordered-to-chaotic transition, accompanied by transitions in correlation timescales, stability characteristics, and signal propagation. By jointly controlling element-level properties and network topology, we establish a unified chaos-latency phase diagram, revealing that small-world connectivity enables low-latency on-off switching of chaos via edge rewiring. Supported by distinct reservoir-computing benchmarks across various topological regimes, our results demonstrate that network topology serves as a reconfigurable parameter for task-specific computation and tunable randomness.