Designing Behaviour in Bio-inspired Robots Using Associative Topologies of Spiking-Neural-Networks

TL;DR

This paper demonstrates how simple spiking neural networks with associative learning can be used to control bio-inspired robots, enabling them to learn obstacle avoidance and reward-seeking behaviors through simulation and physical embodiment.

Contribution

It introduces a novel approach using associative topologies of spiking neural networks with STDP for robot behavior learning, combining simulation and physical robots.

Findings

Robots learned to avoid obstacles and seek rewards.

Neural dynamics were monitored and characterized.

The approach was validated with both simulation and physical robots.

Abstract

This study explores the design and control of the behaviour of agents and robots using simple circuits of spiking neurons and Spike Timing Dependent Plasticity (STDP) as a mechanism of associative and unsupervised learning. Based on a "reward and punishment" classical conditioning, it is demonstrated that these robots learnt to identify and avoid obstacles as well as to identify and look for rewarding stimuli. Using the simulation and programming environment NetLogo, a software engine for the Integrate and Fire model was developed, which allowed us to monitor in discrete time steps the dynamics of each single neuron, synapse and spike in the proposed neural networks. These spiking neural networks (SNN) served as simple brains for the experimental robots. The Lego Mindstorms robot kit was used for the embodiment of the simulated agents. In this paper the topological building blocks are presented as well as the neural parameters required to reproduce the experiments. This paper summarizes the resulting behaviour as well as the observed dynamics of the neural circuits. The Internet-link to the NetLogo code is included in the annex.