Sverrir Thorgeirsson, PhD candidate
David R. Cheriton School of Computer Science
In the past, the Computational Neuroscience Research Group has implemented a biologically realistic action selection system that can perform complex tasks such as sentence parsing, the n-Back task and the Tower of Hanoi. Although our models have successfully performed those tasks, they have so far required human researchers to tune multiple parameters before the models can be expected to exhibit good performance. In this presentation, we show how an improved, parameter-sparse learning rule can help solve that problem.