Supervised Learning of Action Selection in Cognitive Spiking Neuron Models

40th Annual Conference of the Cognitive Science Society, 2018

Terrence C. Stewart, Sverrir Thorgeirsson, Chris Eliasmith


We have previously shown that a biologically realistic spiking neuron implementation of an action selection/execution system (constrained by the neurological connectivity of the cortex, basal ganglia, and thalamus) is capable of performing complex tasks, such as the Tower of Hanoi, n-Back, and semantic memory search. However, because the neural implementation approximates a strict rule-based structure of a production system, such models have involved hand-tweaking of multiple parameters to get the desired behaviour. Here, we show that a simple, local, online learning rule can be used to learn these parameters, resulting in neural models of cognitive behaviours that are more reliable and easier to construct than with prior methods.

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40th Annual Conference of the Cognitive Science Society
Cognitive Science Society


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