context: 1943 - W. McCulloch and W. Pitts formalize the concept of a neural network in a fundamental article on the logical calculus of ideas and nervous activity [1]. At the beginning of his collaboration with Pitts, N. Wiener offers him vacuum tubes as an ideal tool for the implementation of equivalents of neural networks at that time [3]. 1948 - N. Wiener's book on cybernetics is published. The main idea is the presentation of complex biological processes by mathematical models. 1949 - D. Hebb proposes the first learning algorithm. In 1958, F. Rosenblatt invents a single-layer perceptron and demonstrates its ability to solve classification problems [4]. The perceptron has gained popularity - it is used for pattern recognition, weather forecasting, etc .; at that time, it seemed that the creation of full-fledged artificial intelligence was not far off. By the time the perceptron was invented, the discrepancy between the theoretical works of McCulloch and the so-called. “Cybernetics” of Wiener; McCulloch and his followers left the Cyber ​​Club. In 1960, together with his student Hoff, Adalin was developed on the basis of the delta rule (Widrow formula), which immediately began to be used for prediction and adaptive control problems. Adalin was built on the basis of the fundamentally new elements created by them (Widrow - Hoff) - memistors [5]. Now Adalin (adaptive adder) is a standard element of many signal processing systems [6]. In 1963, at the Institute for Information Transmission Problems of the USSR Academy of Sciences. A. P. Petrov conducts a detailed study of the “difficult” problems for the perceptron [7]. This pioneering work in the field of modeling ANNs in the USSR served as a starting point for the complex of ideas of M. M. Bongard - as “a relatively small alteration of the algorithm (perceptron) to fix its shortcomings” [8]. The work of A.P. Petrov and M.M. Bongard greatly contributed to the fact that in the USSR the first wave of euphoria about ANN was smoothed out. In 1969, M. Minsky publishes a formal proof of the limitations of the perceptron and shows that he is unable to solve some problems (the problem of "parity" and "one in a block") related to the invariance of representations. Interest in neural networks drops sharply. In 1972, T. Kohonen independently proposed a new type of neural network capable of functioning as a memory [4]. In 1973, B.V. Khakimov proposed a nonlinear model with splines based synapses and implements it for solving problems in medicine, geology, and ecology [9]. 1974 - Paul J. Verbos [10] and A. I. Galushkin [11] simultaneously invent the back propagation algorithm for training multilayer perceptrons [12]. The invention did not attract much attention. 1975 - Fukushima introduces the Cognitron - a self-organizing network designed for invariant pattern recognition, but this is achieved only by remembering almost all states of the image. 1982 - after a period of oblivion, interest in neural networks increases again. J. Hopfield showed that a neural network with feedback can be a system that minimizes energy (the so-called Hopfield network). Kohonen presents models of a network that learns without a teacher (Kohonen neural network), solves the problems of clustering, data visualization (Kohonen self-organizing map) and other tasks of preliminary data analysis. 1986 - by David I. Rumelhart, J. E. Hinton and Ronald J. Williams [13] and independently and simultaneously by S. I. Bartsev and V. A. Okhonin (Krasnoyarsk group) [14] the method of back propagation of error was rediscovered and significantly developed . An explosion of interest in trained neural networks has begun. 2007 - Jeffrey Hinton at the University of Toronto created deep learning algorithms for multilayer neural networks. The success is due to the fact that Hinton used the Restricted Boltzmann Machine to train the lower layers of the network. Hinton deep learning is a very slow process. It is necessary to use many examples of recognizable images (for example, many faces of people on different backgrounds). After training, you get a ready-made fast-running application that can solve a specific problem (for example, search for faces in an image). The function of searching for people's faces has become standard today and is built into all modern digital cameras. Deep learning technology is actively used by Internet search engines to classify images according to the images contained in them. The artificial neural networks used in recognition can have up to 9 layers of neurons; they are trained on millions of images with a sought image.
question: When did the concept of artificial neural network?
answers.text: 1943