Neuro-Computers and Bio-Computers - an Overview

Background: The basic model of a neural network is the physical structure of the human brain. The idea is simple: if a sufficiently powerful artificial network of synapses is created, the results of functioning similar to the human brain can be expected . Neural network or neurocomputer is one form of artificial intelligence system implementation, which represents a system consisting of a certain number of interconnected processors or nodes, or process elements that we call artificial neurons. Objective: The aim of this article was to draw attention to the necessity of taking about a detailed history of neural networks which are based on copying information processing in synaptic networks in neurons and synapses in the brain. Methods: The author searched scientific literature: the books and published papers deposited in index databases as source for description and explanation the architecture of a neural networks and its represents a specific connection of neurons into a single unit. Resuls and Discussion: The structure of a neural network differs in the number of layers. The first layer is called the input layer, and the last is the output layer, while the layers in between are called hidden layers. Most often, there are three of them. The first layer, or the input layer, is the only layer that receives data from the external environment, the next (hidden) layer forwards the relevant data to the third (output) layer. At the output of the third layer, we get the final result. More complex neural networks have more hidden layers. The layers are fully interconnected. Layers communicate by connecting the output of each neuron from the previous layer to the inputs of all neurons in the next layer. So, each node has several inputs and one output. The strength of the connections by which the neurons are connected is called the weight factor. Conclusion: In the past several books were written and several companies dealing with neurocomputers were founded. The computers were able to successfully adjust the weight coefficients, did not achieve significant practical results. Although neural networks have had an unusual history, they are still in an stage of development. Today, neural networks find a very wide range of applications in various practical areas.