Improving with Practice: A Neural Model of Mathematical Development

Proceedings of the 38th Annual Conference of the Cognitive Science Society, 2016

Sean Aubin, Aaron R. Voelker, Chris Eliasmith


The ability to improve in speed and accuracy as a result of repeating some task is an important hallmark of intelligent biological systems. We model the progression from a counting-based strategy for addition to a recall-based strategy. The model consists of two networks working in parallel: a slower basal ganglia loop, and a faster cortical network. The slow network methodically computes the count from one digit given another, corresponding to the addition of two digits, while the fast network gradually "memorizes" the output from the slow network. The faster network eventually learns how to add the same digits that initially drove the behaviour of the slower network. Performance of this model is demonstrated by simulating a fully spiking neural network that includes basal ganglia, thalamus and various cortical areas. (*) Best Student Paper Award: Computational Modeling Prize in Applied Cognition

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Cognitive Science Society
Proceedings of the 38th Annual Conference of the Cognitive Science Society
Philadelphia, Pennsylvania
Anna Papafragou Dan Grodner Dan Mirman, John Trueswell


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