Feature extraction using an unsupervised neural network
Neural Computation
A Projection Pursuit Algorithm for Exploratory Data Analysis
IEEE Transactions on Computers
The bootstrap widrow-hoff rule as a cluster-formation algorithm
Neural Computation
Toward a biophysically plausible bidirectional Hebbian rule
Neural Computation
Using unlabeled data for learning classification problems
New learning paradigms in soft computing
Unsupervised Learning of Visual Structure
BMCV '02 Proceedings of the Second International Workshop on Biologically Motivated Computer Vision
Probabilistic Reasoning Models for Face Recognition
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
The effect of noise on a class of energy-based learning rules
Neural Computation
Formation of Direction Selectivity in Natural Scene Environments
Neural Computation
A statistical model of cluster stability
Pattern Recognition
Influence function analysis of pca and bcm learning
Neural Computation
A BCM theory of meta-plasticity for online self-reorganizing fuzzy-associative learning
IEEE Transactions on Neural Networks
A general learning rule for network modeling of neuroimmune interactome
WIRN'05 Proceedings of the 16th Italian conference on Neural Nets
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In this paper, we present an objective function formulation of the Bienenstock, Cooper, and Munro (BCM) theory of visual cortical plasticity that permits us to demonstrate the connection between the unsupervised BCM learning procedure and various statistical methods, in particular, that of Projection Pursuit. This formulation provides a general method for stability analysis of the fixed points of the theory and enables us to analyze the behavior and the evolution of the network under various visual rearing conditions. It also allows comparison with many existing unsupervised methods. This model has been shown successful in various applications such as phoneme and 3D object recognition. We thus have the striking and possibly highly significant result that a biological neuron is performing a sophisticated statistical procedure.