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Networks adapt to environmental demands by switching between distinct dynamical behaviors. The activity of frontal-lobe neurons during two-interval discrimination tasks is an example of these adaptable dynamics. Subjects first perceive a stimulus, then hold it in working memory, and finally make a decision by comparing it with a second stimulus. We present a simple mutual-inhibition network model that captures all three task phases within a single framework. The model integrates both working memory and decision making because its dynamical properties are easily controlled without changing its connectivity. Mutual inhibition between nonlinear units is a useful design motif for networks that must display multiple behaviors., In our daily lives, our minds can flit from thought to thought with remarkable speed and flexibility (1). A simplified task that requires rapid shifts between different mental actions is [...]

The article discusses a computational model of the human brain capable of simulating complex behaviors developed by C. Eliasmith et al. Eliasmith and colleagues' model, which is called the Semantic Pointer Architecture Unified Network (Spaun), can observe visual images and indicate its responses with a physical model of an arm. Topics include the challenges of building an artificial brain, such as the complexity of the human brain, the range of tasks Spaun can perform, such as image recognition, and an overview of the cognitive machinery of Spaun.