Winner-take-all discrete recurrent neural networks

Abstract

This paper proposes a discrete recurrent neural network model to implement winnertake-all function. This network model has simple organizations and clear dynamic behaviours. The dynamic properties of the proposed winner-take-all networks are studied in detail. Simulation results are given to show network performance. Since the network model is formulated as discrete time systems, it has advantages for computer simulations over digital simulations of continuous time neural network model. Thus they can be easily implemented in digital hardware. Index Terms | Winner-take-all neural networks, discrete recurrent neural networks, network response time.

Winner-Take-All and Loser-Take-All Circuits: Architectures, Applications and Analytical Comparison

“…These units are among the fundamental blocks for realizing neural networks, data classification/clustering approaches, and image processing algorithms in complementary metal oxide semiconductor (CMOS) technology. Unsupervised learning networks are also implemented using WTA/LTA circuits [15], whose applications span from generative adversarial networks to ladder networks and variational autoencoders [45]. Fuzzy logic control [2,27], rectifiers [12,32,37], artificial neural networks (ANN) [3,36], associative memory [8], neuromorphic [44], vision sensors [40,46], nonlinear filters [31] and telecommunication circuits [5] are among the other applications which contain WTA/LTA units.…”