Multilayered feed-forward neural networks are considered universal approximators and hence extensively been used for function approximation. Function approximation is an instance of supervised learning which is one of the most studied topics in machine learning, artificial neural networks, pattern recognition, and statistical curve fitting. Bidirectional associative memory is another class of n...

This paper studies Hopfield neural networks from the perspective of self-stabilizing distributed computation. Known self-stabilization results on Hopfield networks are surveyed. Key ingredients of the proofs are given. Novel applications of self-stabilization—associative memories and optimization—arising from the context of neural networks are discussed. Two new results at the intersection of H...

Synaptic runaway denotes the formation of erroneous synapses and premature functional decline accompanying activity-dependent learning in neural networks. This work studies synaptic runaway both analytically and numerically in binary-firing associative memory networks. It turns out that synaptic runaway is of fairly moderate magnitude in these networks under normal, baseline conditions. However...

An associative memory AM is a special kind of neural network that allows recalling one output pattern given an input pattern as a key that might be altered by some kind of noise (additive, subtractive or mixed). Most of these models have several constraints that limit their applicability in complex problems such as face recognition (FR) and 3D object recognition (3DOR). Despite of the power of ...

Although Hopfield neural network is one of the most commonly used neural network models for auto-association and optimization tasks, it has several limitations. For example, it is well known that Hopfield neural networks has limited stored patterns, local minimum problems, limited noise ratio, retrieve reverse value of pattern, and shifting and scaling problems. This research will propose multi...

Although Hopfield neural network is one of the most commonly used neural network models for auto-association and optimization tasks, it has several limitations. For example, it is well known that Hopfield neural networks has limited stored patterns, local minimum problems, limited noise ratio, retrieve reverse value of pattern, and shifting and scaling problems. This research will propose multi...

This study empirically compares two distributed connectionist learning models trained to represent an arbitrarily deep stack. One is Pol-lack's Recursive Auto-Associative Memory, a recurrent back propagating neural network that uses a hidden intermediate representation. The other is the Exponential Decay Model, a novel architecture that we propose here, which tries to learn an explicit represen...

This study empirically compares two distributed connectionist learning models trained to represent an arbitrarily deep stack. One is Pol-lack's Recursive Auto-Associative Memory, a recurrent back propagating neural network that uses a hidden intermediate representation. The other is the Exponential Decay Model, a novel architecture that we propose here, which tries to learn an explicit represen...

The efficient realization, using current silicon technology, of Very Large Connection Networks (VLCN) with more than a billion connections requires that these networks exhibit a high degree of communication locality. Real neural networks exhibit significant locality, yet most connectionist/neural network models have little. In this paper, the connectivity requirements of a simple associative ne...

In this paper we present a new associative model of classical conditioning based on a neural network. The new model is compared with a number of other well-known models of classical conditioning. The experiments that are used to evaluate the new model are commonly used and they represent the set of tasks that a model of classical conditioning needs to address in order to be successful. The new ...