NIPS 2006
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Continuous Attractor Neural Networks

Si Wu · Thomas Trappenberg


Continuous attractor neural networks (CANNs) are a special type of recurrent networks that have been studied in many neuro- and cognitive science areas such as modelling hypercolumns, movement generation, spatial navigation, working memory, population coding, attention, saccade initiation and decision making. They have been also applied to engineering problems such as robot control. Such neural field models of the Wilson-Cowan-Amari type, or bump models, are a fundamental type of neural circuitry that underlies the general mechanisms for neural systems encoding continuous stimuli and categorizing objects. The goal of the workshop is to bring together researchers from diverse areas to solidify the existing research on CANNs, identify important issues that need to be solved, and explore their potential applications to artificial learning systems.

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