A Competitive-Layer Model for Feature Binding and Sensory Segmentation
Neural Computation
Estimate of exponential convergence rate and exponential stability for neural networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Global exponential convergence of recurrent neural networks with variable delays
Theoretical Computer Science
Complete Convergence of Competitive Neural Networks with Different Time Scales
Neural Processing Letters
Analysis of Cyclic Dynamics for Networks of Linear Threshold Neurons
Neural Computation
A Winner-Take-All Neural Networks of N Linear Threshold Neurons without Self-Excitatory Connections
Neural Processing Letters
Discrete-time recurrent neural networks with complex-valued linear threshold neurons
IEEE Transactions on Circuits and Systems II: Express Briefs
Analysis of continuous attractors for 2-D linear threshold neural networks
IEEE Transactions on Neural Networks
Representations of continuous attractors of recurrent neural networks
IEEE Transactions on Neural Networks
Permitted and forbidden sets in discrete-time linear threshold recurrent neural networks
IEEE Transactions on Neural Networks
Nontrivial global attractors in 2-D multistable attractor neural networks
IEEE Transactions on Neural Networks
Memory dynamics in attractor networks with saliency weights
Neural Computation
Foundations of implementing the competitive layer model by Lotka-Volterra recurrent neural networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
A Competitive Layer Model for Cellular Neural Networks
Neural Networks
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Multistability is a property necessary in neural networks in order to enable certain applications (e.g., decision making), where monostable networks can be computationally restrictive. This article focuses on the analysis of multistability for a class of recurrent neural networks with unsaturating piecewise linear transfer functions. It deals fully with the three basic properties of a multistable network: boundedness, global attractivity, and complete convergence. This article makes the following contributions: conditions based on local inhibition are derived that guarantee boundedness of some multistable networks, conditions are established for global attractivity, bounds on global attractive sets are obtained, complete convergence conditions for the network are developed using novel energy-like functions, and simulation examples are employed to illustrate the theory thus developed.