Though mathematical ideas underpin the study of neural networks, the author presents the
fundamentals without the full mathematical apparatus. All aspects of the field are tackled …

The Handbook of Neural Computation is a practical, hands-on guide to the design and
implementation of neural networks used by scientists and engineers to tackle difficult and/or …

Patterns of neural activity are systematically elicited as the brain experiences categorical
stimuli and a major challenge is to understand what these patterns represent. Two influential …

Present approaches for computing do not have the perfor-mance, flexibility and reliability of
neural information processing sys-tems. In order to overcome this, conventional computing …

Morphological neural networks are based on a new paradigm for neural computing. Instead
of adding the products of neural values and corresponding synaptic weights, the basic …

The ability of human beings to retrieve information on the basis of associated cues continues
to elicit great interest among researchers. Investigations of how the brain is capable to make …

Learning convergence is demonstrated for networks of nodes which are defined by a
population of values at the vertices of the n-dimensional hypercube. These are functionally …

Fault detection in induction motors is an important task in industry when production greatly
depends of the functioning of the machine. This paper presents a new computational model …

Despite recent progress in memory augmented neural network (MANN) research,
associative memory networks with a single external memory still show limited performance …

Unconventional computing paradigms are typically very difficult to program. By
implementing efficient parallel control architectures such as artificial neural networks, we …