Selectively grouping neurons in recurrent networks of lateral inhibition
Neural Computation
Permitted and forbidden sets in symmetric threshold-linear networks
Neural Computation
A Competitive-Layer Model for Feature Binding and Sensory Segmentation
Neural Computation
Foundations of implementing the competitive layer model by Lotka-Volterra recurrent neural networks
IEEE Transactions on Neural Networks
Cellular Neural Networks: Analysis, Design and Optimization
Cellular Neural Networks: Analysis, Design and Optimization
IEEE Transactions on Neural Networks
Convergence analysis of a deterministic discrete time system of Oja's PCA learning algorithm
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
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This paper discusses a Competitive Layer Model (CLM) for a class of recurrent Cellular Neural Networks (CNNs) from continuous-time type to discrete-time type. The objective of the CLM is to partition a set of input features into salient groups. The complete convergence of such networks in continuous-time type has been discussed first. We give a necessary condition, and a necessary and sufficient condition, which allow the CLM performance existence in our networks. We also discuss the properties of such networks of discrete-time type, and propose a novel CLM iteration method. Such method shows similar performance and storage allocation but faster convergence compared with the previous CLM iteration method (Wersing, Steil, & Ritter, 2001a). Especially for a large scale network with many features and layers, it can significantly reduce the computing time. Examples and simulation results are used to illustrate the developed theory, the comparison between two CLM iteration methods, and the application in image segmentation.