Differential equations and dynamical systems
Differential equations and dynamical systems
Dynamical stability of formation of cortical maps
Dynamic interactions in neural networks
Independent component analysis by general nonlinear Hebbian-like learning rules
Signal Processing - Special issue on neural networks
Self-Organizing Maps
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems
Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems
Neural networks for classification: a survey
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Learning pattern classification-a survey
IEEE Transactions on Information Theory
On self-organizing algorithms and networks for class-separability features
IEEE Transactions on Neural Networks
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A number of well-known unsupervised feature extraction neural network models are present in literature. The development of unsupervised pattern classification systems, although they share many of the principles of the aforementioned network models, has proven to be more elusive. This paper describes in detail a neural network capable of performing class separability through self-organizing Hebbian like dynamics, i.e., the network is able to autonomously find classes of patterns without the help from any external agent. The model is built around a recurrent network performing winner-takes-all competition. Automatic labelling of input data samples is based upon the induced activity pattern after presentation of the sample. Neurons compete against each other through recurrent interactions to code the input sample. Resulting active neurons update their parameters to improve the classification process. The learning dynamics are moreover absolutely stable.