Solving TSP by using Lotka-Volterra neural networks
Neurocomputing
Selective Attention Improves Learning
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part II
Self-emerging action gestalts for task segmentation
KI'09 Proceedings of the 32nd annual German conference on Advances in artificial intelligence
Foundations of implementing the competitive layer model by Lotka-Volterra recurrent neural networks
IEEE Transactions on Neural Networks
A new method based on the CLM of the LV RNN for brain MR image segmentation
Digital Signal Processing
A Competitive Layer Model for Cellular Neural Networks
Neural Networks
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We present a hybrid learning method bridging the fields of recurrent neural networks, unsupervised Hebbian learning, vector quantization, and supervised learning to implement a sophisticated image and feature segmentation architecture. This architecture is based on the competitive layer model (CLM), a dynamic feature binding model, which is applicable on a wide range of perceptual grouping and segmentation problems. A predefined target segmentation can be achieved as attractor states of this linear threshold recurrent network, if the lateral weights are chosen by Hebbian learning. The weight matrix is given by the correlation matrix of special pattern vectors with a structure dependent on the target labeling. Generalization is achieved by applying vector quantization on pair-wise feature relations, like proximity and similarity, defined by external knowledge. We show the successful application of the method to a number of artificial test examples and a medical image segmentation problem of fluorescence microscope cell images.