Closed-form control with spike coding networks

TL;DR

This paper extends Spike Coding Networks to develop a closed-form, biologically plausible spiking control method that is robust, efficient, and suitable for on-chip implementation without learning or optimization.

Contribution

It introduces a novel analytical approach for control with spiking neural networks, combining SCN theory with optimal estimation and control, enabling direct application to control problems.

Findings

Robust control demonstrated on spring-mass-damper system

Effective control of cart-pole system under noise and disturbances

No learning or optimization required for control implementation

Abstract

Efficient and robust control using spiking neural networks (SNNs) is still an open problem. Whilst behaviour of biological agents is produced through sparse and irregular spiking patterns, which provide both robust and efficient control, the activity patterns in most artificial spiking neural networks used for control are dense and regular -- resulting in potentially less efficient codes. Additionally, for most existing control solutions network training or optimization is necessary, even for fully identified systems, complicating their implementation in on-chip low-power solutions. The neuroscience theory of Spike Coding Networks (SCNs) offers a fully analytical solution for implementing dynamical systems in recurrent spiking neural networks -- while maintaining irregular, sparse, and robust spiking activity -- but it's not clear how to directly apply it to control problems. Here, we extend SCN theory by incorporating closed-form optimal estimation and control. The resulting networks work as a spiking equivalent of a linear-quadratic-Gaussian controller. We demonstrate robust spiking control of simulated spring-mass-damper and cart-pole systems, in the face of several perturbations, including input- and system-noise, system disturbances, and neural silencing. As our approach does not need learning or optimization, it offers opportunities for deploying fast and efficient task-specific on-chip spiking controllers with biologically realistic activity.