The Neural Engineering Framework (NEF) and Semantic Pointer Architecture (SPA) provide the theoretical underpinnings of the neural simulation environment Nengo. Nengo has recently been used to build Spaun, a stateof-the-art, large-scale neural model that performs motor, perceptual, and cognitive functions with spiking neurons (Eliasmith et al., 2012). In this tutorial we take the reader through the steps needed to create two simpler, illustrative cognitive models. The purpose of this tutorial is to simultaneously introduce the reader to the SPA and its implementation in Nengo.

The ability to improve in speed and accuracy as a result of repeating some task is an important hallmark of intelligent biological systems. Although gradual behavioral improvements from practice have been modeled in spiking neural networks, few such models have attempted to explain cognitive development of a task as complex as addition. In this work, 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, whereas 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 behavior 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. Consequently, the model incorporates various neuroanatomical data, in terms of brain areas used for calculation and makes psychologically testable predictions related to frequency of rehearsal. Furthermore, the model replicates developmental progression through addition strategies in terms of reaction times and accuracy, and naturally explains observed symptoms of dyscalculia.

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