A silicon model of auditory localization
Neural Computation
Dynamics of analog neural networks with time delay
Advances in neural information processing systems 1
1994 Special Issue: Two-dimensional neural network model of the primate saccadic system
Neural Networks - Special issue: models of neurodynamics and behavior
Modelling the statistical processing of visual information
Neurocomputing
A canonical neural circuit for cortical nonlinear operations
Neural Computation
State-dependent computation using coupled recurrent networks
Neural Computation
Competitive stdp-based spike pattern learning
Neural Computation
Computation with spikes in a winner-take-all network
Neural Computation
Computing the maximum using presynaptic inhibition with glutamate receptors
BVAI'07 Proceedings of the 2nd international conference on Advances in brain, vision and artificial intelligence
Central auditory model for spectral processing
ICASSP'93 Proceedings of the 1993 IEEE international conference on Acoustics, speech, and signal processing: speech processing - Volume II
A systematic method for configuring vlsi networks of spiking neurons
Neural Computation
Dynamic state and parameter estimation applied to neuromorphic systems
Neural Computation
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A winner-take-all mechanism is a device that determines the identity and amplitude of its largest input (Feldman and Ballard 1982). Such mechanisms have been proposed for various brain functions. For example, a theory for visual velocity estimate (Grzywacz and Yuille 1989) postulates that a winner-take-all selects the strongest responding cell in the cortex's middle temporal area (MT). This theory proposes a circuitry that links the directionally selective cells in the primary visual cortex to MT cells, making them velocity selective. Generally, several velocity cells would respond, but only the winner would determine the perception. In another theory, a winner-take-all guides the spotlight of attention to the most salient image part (Koch and Ullman 1985). Also, such mechanisms improve the signal-to-noise ratios of VLSI emulations of brain functions (Lazzaro and Mead 1989). Although computer algorithms for winner-take-all mechanisms exist (Feldman and Ballard 1982; Koch and Ullman 1985), good biologically motivated models do not. A candidate for a biological mechanism is lateral (mutual) inhibition (Hartline and Ratliff 1957). In some theoretical mutual-inhibition networks, the inhibition sums linearly to the excitatory inputs and the result is passed through a threshold non linearity (Hadeler 1974). However, these networks work only if the difference between winner and losers is large (Koch and Ullman 1985). We propose an alternative network, in which the output of each element feeds back to inhibit the inputs to other elements. The action of this presynaptic inhibition is nonlinear with a possible biophysical substrate. This paper shows that the new network converges stably to a solution that both relays the winner's identity and amplitude and suppresses information on the losers with arbitrary precision. We prove these results mathematically and illustrate the effectiveness of the network and some of its variants by computer simulations.