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This study explores the design and control of the behaviour of agents androbots using simple circuits of spiking neurons and Spike Timing DependentPlasticity (STDP) as a mechanism of associative and unsupervised learning.Based on a "reward and punishment" classical conditioning, it is demonstratedthat these robots learnt to identify and avoid obstacles as well as to identifyand look for rewarding stimuli. Using the simulation and programmingenvironment NetLogo, a software engine for the Integrate and Fire model wasdeveloped, which allowed us to monitor in discrete time steps the dynamics ofeach single neuron, synapse and spike in the proposed neural networks. Thesespiking neural networks (SNN) served as simple brains for the experimentalrobots. The Lego Mindstorms robot kit was used for the embodiment of thesimulated agents. In this paper the topological building blocks are presentedas well as the neural parameters required to reproduce the experiments. Thispaper summarizes the resulting behaviour as well as the observed dynamics ofthe neural circuits. The Internet-link to the NetLogo code is included in theannex.

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