Autoassociative learning in relaxation labeling networks

An asymmetric associative memory model based on relaxation labeling processes

Mirror Image Learning for Autoassociative Neural Networks

This paper studies on the mirror image learning algorithm for the autoassociative neural networks and evaluates the performance by handwritten numeral recognition test. Each of the autoassociative networks is first trained independently for each class using the feature vector of the class. Then the mirror image learning algorithm is applied to enlarge the learning sample of each class by mirror...

Relaxation Labeling Networks for the Maximum Clique Problem

Relaxation labeling networks are a class of parallel distributed computational models extremely popular in computer vision and pattern recognition. Despite their original heuristic derivation, they possess interesting dynamical properties and learning abilities, and exhibit also a certain biological plausibility. In this paper, it is shown how to take advantage of the properties of these models...

An Autoassociative Neural Network Model of Paired-Associate Learning

Hebbian heteroassociative learning is inherently asymmetric. Storing a forward association, from item A to item B, enables recall of B (given A), but does not permit recall of A (given B). Recurrent networks can solve this problem by associating A to B and B back to A. In these recurrent networks, the forward and backward associations can be differentially weighted to account for asymmetries in...

Real time unsupervised learning of visual stimuli in neuromorphic VLSI systems

Neuromorphic chips embody computational principles operating in the nervous system, into microelectronic devices. In this domain it is important to identify computational primitives that theory and experiments suggest as generic and reusable cognitive elements. One such element is provided by attractor dynamics in recurrent networks. Point attractors are equilibrium states of the dynamics (up t...