Neural networks and their learning algorithms

For many years, scientists have tried to learn the structure of the brain and discover how it works. Unfortunately, it still remains a fascinating riddle not solved completely. Based on observation of people crippled during different wars or injured in accidents, the scientists could assess the specialization of particular fragments of the brain. It was found, for example, that the left hemisphere is responsible for controlling the right hand, whereas the right hemisphere – for the left hand. The scientists still do not have any detailed information on higher mental functions.We can assume hypothetically that the left hemisphere controls speech function and scientific thinking, whereas the right hemisphere is its opposite as it manages artistic capabilities, spatial imagination etc. The nervous system is made of cells called neurons. There are about 100 billion of them in the human brain. The functioning of a single neuron consists in the flow of so-called nerve impulses. The impulse induced by a specific stimulus encountering a neuron causes its spreading along all its dendrones. As a result, a muscle contraction can occur or another neuron can be stimulated. Why, then, appropriately connected artificial neurons could not, instead of controlling muscles, manage, for example, the work of a device or solve various problems requiring intelligence? This chapter discusses artificial neural networks. We will present a mathematical model of a single neuron, various structures of artificial neural networks and their learning algorithms.