# Evolving Spiking Neural Networks for Nonlinear Control Problems

## TL;DR

This paper introduces a recurrent spiking neural network controller that effectively solves nonlinear control problems in continuous domains, leveraging topology evolution and novel decoding mechanisms for improved learning speed.

## Contribution

It presents a new bioinspired spiking neural network controller with topology evolution and decoding strategies for nonlinear control tasks.

## Key findings

- Faster learning compared to sigmoidal neural networks
- Effective decoding of continuous signals from spikes
- Successful application to classic nonlinear control problems

## Abstract

Spiking Neural Networks are powerful computational modelling tools that have attracted much interest because of the bioinspired modelling of synaptic interactions between neurons. Most of the research employing spiking neurons has been non-behavioural and discontinuous. Comparatively, this paper presents a recurrent spiking controller that is capable of solving nonlinear control problems in continuous domains using a popular topology evolution algorithm as the learning mechanism. We propose two mechanisms necessary to the decoding of continuous signals from discrete spike transmission: (i) a background current component to maintain frequency sufficiency for spike rate decoding, and (ii) a general network structure that derives strength from topology evolution. We demonstrate that the proposed spiking controller can learn significantly faster to discover functional solutions than sigmoidal neural networks in solving a classic nonlinear control problem.