Proceedings of the Annual Meeting of the Cognitive Science Society

We have previously shown that a biologically realistic spikingneuron implementation of an action selection/executionsystem (constrained by the neurological connectivity of thecortex, basal ganglia, and thalamus) is capable of performingcomplex tasks, such as the Tower of Hanoi, n-Back, andsemantic memory search. However, because the neuralimplementation approximates a strict rule-based structure of aproduction system, such models have involved hand-tweakingof multiple parameters to get the desired behaviour. Here, weshow that a simple, local, online learning rule can be used tolearn these parameters, resulting in neural models of cognitivebehaviours that are more reliable and easier to construct thanwith prior methods.