In general a biological neural network is composed of a group or groups of chemically connected or functionally associated neurons. A single neuron may be connected to many other neurons and the total number of neurons and connections in a network may be extensive. These connections are called synapses, are usually formed from axons to dendrites, though dendrodendritic microcircuits and other connections are possible. Apart from the electrical signaling, there are other forms of signaling that arise from neurotransmitter diffusion, which have an effect on electrical signaling. As such, neural networks are extremely complex (Arbib 2002). Now a day the term neural network often refers to artificial neural networks, which are composed of artificial neurons or nodes.
Biological neural networks which are made up of real biological neurons. These Biological neural networks are connected or functionally related in the peripheral nervous system or the central nervous system. They are often identified as groups of neurons that perform a specific physiological function in laboratory analysis.
Artificial neural networks are made up of interconnecting artificial neurons that mimic the properties of biological neurons. Artificial neural networks may either be used to gain an understanding of biological neural networks, or for solving artificial intelligence problems without necessarily creating a model of a real biological system. The real, biological nervous system is highly complex and includes some features that may seem superfluous based on an understanding of artificial networks.
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 analyze. After analyzing, this expert answers the 'what if' questions.
Other advantages of Neural Network include:
Adaptive learning: An ability to learn how to do tasks based on the data given for training or initial experience.
Self-Organization: An Artificial Neural Network can create its own organization or representation of the information it receives during learning time.
Real Time Operation: Artificial Neural Network computations may be carried out in parallel, and special hardware devices are being designed and manufactured which take advantage of this capability.
Fault Tolerance via Redundant Information Coding: Partial destruction of a leads to the corresponding degradation of performance. However, some network capabilities may be retained even with major network damage.
Biological Neural Networks
Most living creatures, which have the ability to adapt to a changing environment, need a controlling unit which is able to learn. Higher developed animals and humans use very complex networks of highly specialized neurons to perform this task.
The control unit or brain can be divided in different anatomic and functional sub-units, each having