Artificial Neural Networks

Artificial Neural Networks:
The interest in neural networks comes from the networks’ ability to
mimic human brain as well as its ability to learn and respond. As a result,
neural networks have been used in a large number of applications and
have proven to be effective in performing complex functions in a variety
of fields. These include pattern recognition, classification, vision, control
systems, and prediction. Adaptation or learning is a major focus of neural
net research that provides a degree of robustness to the NN model.
Biological Neuron:
Artificial neuron is a basic building block of every artificial neural
network. Its design and functionalities are derived from observation of a
biological neuron that is basic building block of biological neural
networks (systems) which includes the brain, spinal cord and peripheral
ganglia. Similarities in design and functionalities can be seen in Fig.1.
where the left side of a figure represents a biological neuron with its
soma, dendrites and axon and where the right side of a figure represents
an artificial neuron with its inputs, weights, transfer function, bias and
outputs.
In case of biological neuron information comes into the neuron via
dendrite, soma processes the information and passes it on via axon.
In case of artificial neuron the information comes into the body of
an artificial neuron via inputs that are weighted (each input can be individually multiplied with a weight).