Neural Networks

Artificial neural networks are among the most powerful learning models. They have the

versatility to approximate a wide range of complex functions representing multidimensional

input-output maps. Neural networks also have inherent adaptability, and can

perform robustly even in noisy environments.

An Artificial Neural Network (ANN) is an information processing paradigm that is

inspired by the way biological nervous systems, such as the brain, process information.

The key element of this paradigm is the novel structure of the information processing

system. It is composed of a large number of highly interconnected simple processing

elements (neurons) working in unison to solve specific problems. ANNs, like people,

learn by example. An ANN is configured for a specific application, such as pattern

recognition or data classification, through a learning process. Learning in biological

systems involves adjustments to the synaptic connections that exist between the neurons.

This is true of ANNs as well. ANNs can process information at a great speed owing to

their highly massive parallelism.

Neural networks, with their remarkable ability to derive meaning from complicated or

imprecise data, can be used to extract patterns and detect trends that are too complex to

be noticed by either humans or other computer techniques. A trained neural network can

be thought of as an "expert" in the category of information it has been given to analyse.

This expert can then be used to provide projections given new situations of interest and

answer "what if" questions. Other advantages include:

1. Adaptive learning: An ability to learn how to do tasks based on the data given for

training or initial experience.

2. Self-Organisation: An ANN can create its own organisation or representation of

the information it receives during learning time.

3. Real Time Operation: ANN computations may be carried out in parallel, and

special hardware devices are being designed and manufactured which take

advantage of this capability.

4. Fault Tolerance via Redundant Information Coding: Partial destruction of a

network leads to the corresponding degradation of performance. However, some

network capabilities may be retained even with major network damage.

Biological Neural Networks

Much is still unknown about how the brain trains itself to process information, so theories

abound. In the human brain, a typical neuron collects signals from others through a host

of fine structures called dendrites. The neuron sends out spikes of electrical activity

through a long, thin stand known as an axon, which splits into thousands of branches. At

the end of each branch, a structure called a synapse converts the activity from the axon

into electrical effects that inhibit or excite activity from the axon into electrical effects

that inhibit or excite activity.

Artificial Neural Networks

Artificial neural networks are represented by a set of nodes, often arranged in layers, and

a set of weighted directed links connecting them. The nodes are equivalent to neurons,

while the links denote synapses. The nodes are the information processing units and the

links acts as communicating media.