At the end of each branch, a synapse converts the activity from the axon into electrical effects that inhibit or excite activity in the connected neurons. Learning occurs by changing the effectiveness of the synapses so that the influence of one neuron on anther changes. Neural networks consist of many single units which are analogous to the brain s cells. Each of these units is connected to many other units using weighted links, similar to the dendrites of natural neurons. An artificial neural network unit receives its inputs,which are multiplied by their respective weights and summed together, then performs some mathematical equations and passes the result along to other units as part of their input pattern.
Neural networks take a different approach to problem solving than that of conventional computers.
Conventional computers use an algorithmic approach, i.e. the computer follows a set of instructions in order to solve a problem. Unless the specific steps that the computer needs to follow are known the computer cannot solve the problem. That restricts the problem solving capability of conventional computers to problems that we already understand and know how to solve. There are many tasks for which algorithms do not yet exist or for which it is virtually impossible to document a series of logical or arithmetic steps that will provide the answer. Some examples include a handwritten character reader, a system that can identify enemy airplanes or a system capable of recognizing continuous speech, regardless of who is speaking.