Winner-take-all Neural Network Research Articles

Design of Multi-Competitors Winner-Take-All Neural Networks Based on DNA Strand Displacement for Molecular Pattern Recognition

DNA strand displacement technology (DSDT) provides flexible and powerful technical support for DNA molecular computing. DNA-based neural networks with Winner-Take-All (WTA) strategy has a great potential for nonlinear calculation. However, so far it has been limited to achieving the simultaneous competition of two competitors. Optimizing the calculation model and reducing system response time to recognize complex and functional molecular patterns remains a huge challenge. Here a novel neural network with WTA strategy based on DSDT was constructed, which allowed three competitors to participate in the competition at the same time. Firstly, the feasibility of the three-competitor WTA neural network was proved by 9-bit pattern recognition. Then the three-competitors WTA neural network was further extended to larger scale pattern recognition, which successfully recognized 64-bit letters A, B, and C and 100-bit handwritten digits 0, 2, and 4, respectively. Simulations showed that when recognizing the same target patterns with same number bits, compared with two-competitors WTA neural network, the three-competitors WTA network only used down to two-thirds DNA strands, and the system response time was reduced by more than ten times. This paper demonstrated the efficient recognition ability of the three-competitor WTA neural network, which is expected to be used to identify more complex information.

Versatile networks of simulated spiking neurons displaying winner-take-all behavior.

We describe simulations of large-scale networks of excitatory and inhibitory spiking neurons that can generate dynamically stable winner-take-all (WTA) behavior. The network connectivity is a variant of center-surround architecture that we call center-annular-surround (CAS). In this architecture each neuron is excited by nearby neighbors and inhibited by more distant neighbors in an annular-surround region. The neural units of these networks simulate conductance-based spiking neurons that interact via mechanisms susceptible to both short-term synaptic plasticity and STDP. We show that such CAS networks display robust WTA behavior unlike the center-surround networks and other control architectures that we have studied. We find that a large-scale network of spiking neurons with separate populations of excitatory and inhibitory neurons can give rise to smooth maps of sensory input. In addition, we show that a humanoid brain-based-device (BBD) under the control of a spiking WTA neural network can learn to reach to target positions in its visual field, thus demonstrating the acquisition of sensorimotor coordination.

A fast static security assessment method based on radial basis function neural networks using enhanced clustering

Power system security is one of the major concerns in recent years due to the deregulation of power systems which are forced to operate under stressed operating conditions. This paper presents an enhanced radial basis function neural network (ERBFNN) and winner-take-all neural network (WTANN) to examine whether the power system is secure under steady-state operating conditions. Hidden layer units have been selected with the proposed algorithm which has the advantage of being able to automatically choose optimal unit centers and distances. The proposed approach to contingency analysis was found to be suitable for fast voltage and line-flow contingency screening. The generalization capability of the proposed method was able to identify unknown contingencies with large range of operating conditions and changes in network topology. A feature extraction technique based on class separability index and correlation coefficient has been employed to identify the inputs and dimensional reduction for the ERBFNN and WTANN networks. The advantages of this method are simplicity of algorithm and high accuracy in classification. Case studies with IEEE 14-bus, IEEE 30-bus and IEEE 118-bus power systems are used to illustrate the good performance of the proposed method.

Dynamic Analysis of a General Class of Winner-Take-All Competitive Neural Networks

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 a WTA 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.

Single-electron winner-take-all network

A winner-take-all (WTA) single-electron neuron is developed for the first time. This new single-electron circuit is proposed in order to implement a WTA neural network with lateral inhibition architecture. An expression for the neuron's activation function is presented. Furthermore, a dot pattern recognition task is successfully performed by the implemented network considering effects such as offset charges and co-tunnelling.

Dynamics of a Winner-Take-All Neural Network

This paper describes a neural network with lateral inhibition, which exhibits dynamic winner-take-all (WTA) behavior. The equations of this network model a current input MOSFET WTA circuit, which motivates the discussion. A very general sufficient condition for the network to have a WTA equilibrium point is obtained and sufficient conditions for the network to converge to the WTA point are presented. This gives explicit expressions for the resolution and lower bound of the input currents. We also show 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 speed up procedure for the decision making: as soon as it gets into the region, the winner can be picked up. Finally, we show that this WTA neural network has a self-resetting property. Copyright © 1996 Elsevier Science Ltd

A general mean-based iterative winner-take-all neural network

In this paper, a new iterative winner-take-all (WTA) neural network is developed and analyzed. The proposed WTA neural net with one-layer structure is established under the concept of the statistical mean. For three typical distributions of initial activations, the convergence behaviors of the existing and the proposed WTA neural nets are evaluated by theoretical analyses and Monte Carlo simulations. We found that the suggested WTA neural network on average requires fewer than Log(2)M iterations to complete a WTA process for the three distributed inputs, where M is the number of competitors. Furthermore, the fault tolerances of the iterative WTA nets are analyzed and simulated. From the view points of convergence speed, hardware complexity, and robustness to the errors, the proposed WTA is suitable for various applications.