NEURAL NETWORKS: NEURAL NETWORKS

The recent rise of interest in neural networks has its roots in the recognition that the
brain performs computations in a different manner than do conventional digital computers.
Computers are extremely fast and precise at executing sequences of instructions that have
been formulated for them. A human information process sing system is composed of
neurons switching at speeds about a million times slower than computer gates. Yet, humans
are more efficient than computers at computationally complex tasks such as speech
understanding. Moreover, not only humans, but also even animals, can process visual
information better than the fastest computers.
Artificial neural systems, or neural networks (NN), are physical cellular systems,
which can acquire, store, and utilize experiential knowledge. The knowledge is in the form
of stable states or mappings embedded in networks that can be recalled in response to the
presentation cues. Neural network processing typically involves dealing with large-scale
problems in terms of dimensionality, amount of data handled, and the volume of simulation
or neural hardware processing. This large-scale approach is both essential and typical for
real-life applications. By keeping view of all these, the research community has made an
effort in designing and implementing the various neural network models for different
applications.