Spiking neural controllers for pushing objects around
Razvan V. Florian
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.
In S. Nolfi et al. (eds.), Proceedings of the Ninth International Conference on the Simulation of Adaptive Behavior (SAB'06). Lecture Notes in Artificial Intelligence 4095, pp. 570-581, 2006.