Spiking neural controllers for pushing objects around

Răzvan V. Florian

Full text: http://dx.doi.org/10.1007/11840541_47

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Abstract

We evolve spiking neural networks that implement a seek-push-release drive for a simple simulated agent interacting with objects. The evolved agents display minimally-cognitive behavior, by switching as a function of context between the three sub-behaviors and by being able to discriminate relative object size. The neural controllers have either static synapses or synapses featuring spike-timing-dependent plasticity (STDP). Both types of networks are able to solve the task with similar efficacy, but networks with plastic synapses evolved faster. In the evolved networks, plasticity plays a minor role during the interaction with the environment and is used mostly to tune synapses when networks start to function.

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