Please note that the content of this book primarily consists of articles available from Wikipedia or other free sources online. In the theory of artificial neural networks winner-take-all networks are a case of competitive learning in recurrent neural networks. Output nodes in the network mutually inhibit each other, while simultaneously activating themselves through reflexive connections. After some time, only one node in the output layer will be active, namely the one corresponding to the strongest input. Thus the network uses nonlinear inhibition to pick out the largest of a set of inputs. Winner-take-all is a general computational primitive that can be implemented using different types of neural network models, including both continuous-time and spiking networks (Grossberg, 1973; Oster et al. 2009). Winner-take-all networks are commonly used in computational models of the brain, particularly for distributed decision-making in the cortex. Important examples include hierarchical models of vision (Riesenhuber et al. 1999), and models of selective attention and recognition (Carpenter and Grossberg, 1987; Itti et al. 1998).

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