Analysis of Learning Action Selection Parameters in a Neural Cognitive Model

International Conference on Cognitive Modelling, 2018

Sverrir Thorgeirsson, Terrence C. Stewart, Chris Eliasmith


In our previous work, we have 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 paper, we show that an improved, parameter-sparse learning rule can be applied to a cognitive sequencing task.

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