In the human brain, information is passed between the neurons in form of electrical stimulation along the dendrites. If a certain amount of stimulation is received by a neuron, it generates an output to all other connected neurons and so information takes its way to its destination where some reaction will occur. If the incoming stimulation is too low, no output is generated by the neuron and the information's further transport will be blocked.
Explaining how the human brain learns certain things is quite difficult and nobody knows it exactly.
It is supposed that during the learning process the connection structure among the neurons is changed, so that certain stimulations are only accepted by certain neurons. This means, there exist firm connections between the neural cells that once have learned a specific fact, enabling the fast recall of this information.
If some related information is acquired later, the same neural cells are stimulated and will adapt their connection structure according to this new information.
On the other hand, if a specific information isn't recalled for a long time, the established connection structure between the responsible neural cells will get more "weak". This had happened if someone "forgot" a once learned fact or can only remember it vaguely.
As mentioned before, neural nets try to simulate the human brain's ability to learn. That is, the artificial neural net is also made of neurons and dendrites. Unlike the biological model, a neural net has an unchangeable structure, built of a specified number of neurons and a specified number of connections between them (called "weights"), which have certain values.
What changes during the learning process are the values of those weights. Compared to the original this means:
Incoming information "stimulates" (exceeds a specified threshold value of) certain neurons that pass the information to connected neurons or prevent further transportation along the weighted connections. The value of a weight will be increased if information should be transported and decreased if not.
While learning different inputs, the weight values are changed dynamically until their values are balanced, so each input will lead to the desired output.
The training of a neural net results in a matrix that holds the weight values between the neurons. Once a neural net had been trained correctly, it will probably be able to find the desired output to a given input that had been learned, by using these matrix values.
I said "probably". That is sad but true, for it can't be guaranteed that a neural net will recall the correct results in any case.
Very often there is a certain error left after the learning process, so the generated output is only a good approximation to the perfect output in most cases.
The following sections introduce several learning algorithms for neural networks.
