The design and analysis of parallel algorithms
The design and analysis of parallel algorithms
On the K-winners-take-all-network
Advances in neural information processing systems 1
Winner-take-all networks of O(N) complexity
Advances in neural information processing systems 1
Feature discovery by competitive learning
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
The logic of activation functions
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
On a magnitude preserving iterative MAXnet algorithm
Neural Computation
Recurrent Algorithms for Selecting the Maximum Input
Neural Processing Letters
COMAX: A Cooperative Method for Determining the Position of the Maxima
Neural Processing Letters
IEEE Transactions on Neural Networks
ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part I
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A nearly cost-optimal winner-take-all (WTA) neural network derived from a constant-time sorting network is presented. The resultant WTA network has connection complexity ${\rm O}({\rm n}^{2^{\rm s}/(2^{\rm s} - 1)})$ where s is the depth of cascaded sorting networks. Application of the WTA network to other problems such as nonbinary majority is also included.