Convergent activation dynamics in continuous time networks
Neural Networks
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
Nonlinear systems analysis (2nd ed.)
Nonlinear systems analysis (2nd ed.)
1994 Special Issue: Winner-take-all networks for physiological models of competitive learning
Neural Networks - Special issue: models of neurodynamics and behavior
Current mode circuits for programmable WTA neural network
Analog Integrated Circuits and Signal Processing - Special issue on ICECS-99
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Sensory Neural Networks: Lateral Inhibition
Sensory Neural Networks: Lateral Inhibition
Stability analysis of dynamical neural networks
IEEE Transactions on Neural Networks
Another K-winners-take-all analog neural network
IEEE Transactions on Neural Networks
A general mean-based iterative winner-take-all neural network
IEEE Transactions on Neural Networks
Optoelectronic winner-take-all VLSI shunting neural network
IEEE Transactions on Neural Networks
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This paper studies a general class of dynamical neural networks with lateral inhibition, exhibiting winner-take-all (WTA) behavior. These networks are motivated by a metal-oxide-semiconductor field effect transistor (MOSFET) implementation of neural networks, in which mutual competition plays a very important role.We show that for a fairly general class of competitive neural networks, WTA behavior exists. Sufficient conditions for the network to have aWTA equilibrium are obtained, and rigorous convergence analysis is carried out. The conditions for the network to have the WTA behavior obtained in this paper provide design guidelines for the network implementation and fabrication. We also demonstrate that whenever the network gets into the WTA region, it will stay in that region and settle down exponentially fast to the WTA point. This provides a speeding procedure for the decision making: as soon as it gets into the region, the winner can be declared. Finally, we show that this WTA neural network has a self-resetting property, and a resetting principle is proposed.