Designing Behaviour in Bio-inspired Robots Using Associative Topologies of Spiking-Neural-Networks
This work addresses behavior control in bio-inspired robots for robotics researchers, but it is incremental as it applies known neural methods to simple robotic tasks.
The study tackled designing robot behavior using spiking neural networks with STDP for unsupervised learning, demonstrating that robots learned to avoid obstacles and seek rewarding stimuli through simulations and Lego Mindstorms experiments.
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.