A Brief history of ANN

Artificial neural networks:

ALSO REFFERRED TO AS NEUROMORPHIC systems, artificial intelligence and parallell distributed processing, artificial neural networks (ANNs) are an attempt at mimicing the patterns of the human mind.
A brief history of ANNs
In the early 1940's scientists came up with the hypothesis that neurons—fundamental, active cells in all animal nervous systems—might be regarded as devices for manipulating binary numbers—computers.
Early attempts at building ANNs required a great deal of computer power to replicate a few hundred neurons. Consider that an ant's nervous system is composed of over 20,000 neurons and a human being's nervous system consists of over 100 billion neurons.
More recently, ANNs are being applied to an increasing number of complex real world problems, such as pattern recognition and classification, with the ability to generalize and make decisions about imprecise data. They offer solutions to a variety of classification problems such as speech, character, and signal recognition, as well as prediction and system modeling where physical processes are not well understood or are highly complex (Hassoun, 2000).

This is a brightly glowing neuron and behind it is the layout for an artficial neural network.
Neurons 101
The single cell neuron consists of the cell body, or soma, the dendrites, and the axon. The dendrites receive signals from the axons of other neurons. The small space between the axon of one neuron and the dendrite of another is the synapse. The dendrites conduct impulses toward the soma and the axon conducts impulses away from the soma.
The function of the neuron is to integrate the input it receives through its synapses on its dendrites and either generate an action potential or not (Chicurrel, 1995).

ANNs 101
Neural Networks use a set of processing elements (or nodes) loosely analogous to neurons in the brain (hence the name, neural networks.) These nodes are interconnected in a network that can then identify patterns in data as it is exposed to the data. In a sense, the network learns from experience just as people do. This distinguishes neural networks from traditional computing programs, that simply follow instructions in a fixed sequential order.
Roll your mouse over the picture of the neuron above to see the basic layout or concept behind artificial neural networks. The bottom layer represents the input layer, in this case with 5 inputs. In the middle is something called the hidden layer, with a variable number of nodes. It is the hidden layer that performs much of the work of the network. The output layer in this case has two nodes, representing output values we are trying to determine from the inputs (Hassoun, 2000).

Possible futures of ANNs
The secrets of the human mind still elude us no matter how much we boost proccessing speed and capacity. That said, neural networks have given us great advancements in tasks such as Optical Character Recognition, financial forecasting and even in medical diagnosis.
For any group in which a known interrelationship exists with an unknown outcome there is a possibility that ANNs will be helpful. While the need for computer-based training and e-learning courses grows, the need to develop computer systems that can learn by themselves and improve decision-making will be an ongoing goal of information technology.