Proceedings of the Annual Meeting of the Cognitive Science Society

The ability to improve in speed and accuracy as a result of re-peating some task is an important hallmark of intelligent bio-logical systems. Although gradual behavioural improvementsfrom practice have been modelled in spiking neural networks,few such models have attempted to explain cognitive devel-opment of a task as complex as addition. In this work, wemodel the progression from a counting-based strategy for ad-dition to a recall-based strategy. The model consists of twonetworks working in parallel: a slower basal ganglia loop, anda faster cortical network. The slow network methodically com-putes the count from one digit given another, correspondingto the addition of two digits, while the fast network gradually“memorizes” the output from the slow network. The faster net-work eventually learns how to add the same digits that initiallydrove the behaviour of the slower network. Performance ofthis model is demonstrated by simulating a fully spiking neu-ral network that includes basal ganglia, thalamus and variouscortical areas. Consequently, the model incorporates variousneuroanatomical data, in terms of brain areas used for calcula-tion and makes psychologically testable predictions related tofrequency of rehearsal. Furthermore, the model replicates de-velopmental progression through addition strategies in termsof reaction times and accuracy, and naturally explains observedsymptoms of dyscalculia.