Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
Convergent activation dynamics in continuous time networks
Neural Networks
On the K-winners-take-all-network
Advances in neural information processing systems 1
Dynamics of a winner-take-all neural network
Neural Networks
Introduction to Neural and Cognitive Modeling
Introduction to Neural and Cognitive Modeling
On the Computational Power of Winner-Take-All
Neural Computation
Computation with spikes in a winner-take-all network
Neural Computation
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We construct a novel discrimination network using differentiating units for maximum selection. In contrast to traditional competitive architectures like MAXNET the discrimination network does not only signal the winning unit, but also provides information about its evidence. In particular, we show that a discrimination network converges to a stable state within finite time and derive three characteristics: intensity normalization (P1), contrast enhancement (P2), and evidential response (P3). In order to improve the accuracy of the evidential response we incorporate distributed redundancy into the network. This leads to a system which is not only robust against failure of single units and noisy data, but also enables us to sharpen the focus on the problem given in terms of a more accurate evidential response. The proposed discrimination network can be regarded as a connectionist model for competitive learning by evidence.