From 65438 to 0945, the design team led by von Neumann successfully trial-produced the stored program electronic computer, marking the beginning of the electronic computer era. 1948, in his research work, he compared the fundamental difference between the human brain structure and the stored program computer, and proposed the regenerative automata network structure composed of simple neurons. However, due to the rapid development of instruction storage computer technology, he abandoned the new way of neural network research and continued to devote himself to the research of instruction storage computer technology, and made great contributions in this field. Although von Neumann's name is associated with ordinary computers, he is also one of the pioneers in the study of artificial neural networks.
At the end of 1950s, Rosenblat designed and manufactured a kind of "perceptron", which is a multi-layer neural network. This work pushes the research of artificial neural network from theoretical discussion to engineering practice for the first time. At that time, many laboratories around the world followed the example of making perceptrons and applied them to the study of character recognition, speech recognition, sonar signal recognition and learning and memory problems. However, the research climax of artificial neural network did not last long, and many people gave up the research work in this field one after another, because the development of digital computer was in its heyday at that time, and many people mistakenly thought that digital computer could solve all problems in artificial intelligence, pattern recognition, expert system and so on, which made the work of perceptron ignored. Secondly, the level of electronic technology at that time was relatively backward, and the main components were electron tubes or transistors. The neural network they made is huge and expensive, so it is impossible to be similar to the real neural network in scale. In addition, in a book called Perceptron from 65438 to 0968, it is pointed out that the function of linear perceptron is limited, it can not solve basic problems such as XOR, and multi-layer networks can not find an effective calculation method. These arguments have prompted a large number of researchers to lose confidence in the future of artificial neural networks. In the late 1960s, the research of artificial neural network entered a low tide.
In addition, in the early 1960s, Widrow proposed an adaptive linear element network, which is a linear weighted summation threshold network with continuous values. Later, a nonlinear multi-layer adaptive network was developed on this basis. At that time, although these works were not labeled with the name of neural network, they were actually an artificial neural network model.
With the decline of people's interest in perceptron, the research of neural network has been silent for a long time. In the early 1980s, the manufacturing technology of analog and digital mixed VLSI was raised to a new level and put into practical application. In addition, the development of digital computers has encountered difficulties in several application fields. This background shows that the time is ripe to find a way out from artificial neural network. Hopfield, an American physicist, published two papers on artificial neural networks in the Proceedings of the National Academy of Sciences in 1982 and 1984, which caused great repercussions. People have re-recognized the power of neural network and the reality of its application. Immediately, a large number of scholars and researchers carried out further work around the method proposed by Hopfield, forming a research upsurge of artificial neural networks since the mid-1980s.