Winner-take-all networks of O(N) complexity
Advances in neural information processing systems 1
1994 Special Issue: Winner-take-all networks for physiological models of competitive learning
Neural Networks - Special issue: models of neurodynamics and behavior
Dynamics of a winner-take-all neural network
Neural Networks
A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object selection based on oscillatory correlation
Neural Networks
The Handbook of Brain Theory and Neural Networks
The Handbook of Brain Theory and Neural Networks
A Current-Mode Hysteretic Winner-take-all Network, with Excitatory and Inhibitory Coupling
Analog Integrated Circuits and Signal Processing
Winner-Take-All Networks with Lateral Excitation
Analog Integrated Circuits and Signal Processing
A Normalizing aVLSI Network with Controllable Winner-Take-All Properties
Analog Integrated Circuits and Signal Processing
Biophysiologically plausible implementations of the maximum operation
Neural Computation
Discrimination networks for maximum selection
Neural Networks
On the Computational Power of Winner-Take-All
Neural Computation
A winner-take-all mechanism based on presynaptic inhibition feedback
Neural Computation
Adaptive WTA With an Analog VLSI Neuromorphic Learning Chip
IEEE Transactions on Neural Networks
Abstract stimulus-specific adaptation models
Neural Computation
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The winner-take-all (WTA) computation in networks of recurrently connected neurons is an important decision element of many models of cortical processing. However, analytical studies of the WTA performance in recurrent networks have generally addressed rate-based models. Very few have addressed networks of spiking neurons, which are relevant for understanding the biological networks themselves and also for the development of neuromorphic electronic neurons that commmunicate by action potential like address-events. Here, we make steps in that direction by using a simplified Markov model of the spiking network to examine analytically the ability of a spike-based WTA network to discriminate the statistics of inputs ranging from stationary regular to nonstationary Poisson events. Our work extends previous theoretical results showing that a WTA recurrent network receiving regular spike inputs can select the correct winner within one interspike interval. We show first for the case of spike rate inputs that input discrimination and the effects of self-excitation and inhibition on this discrimination are consistent with results obtained from the standard rate-based WTA models. We also extend this discrimination analysis of spiking WTAs to nonstationary inputs with time-varying spike rates resembling statistics of real-world sensory stimuli. We conclude that spiking WTAs are consistent with their continuous counterparts for steady-state inputs, but they also exhibit high discrimination performance with nonstationary inputs.