Initial study of general theory of complex systems: physical basis and philosophical understanding

Abstract

The fundamental difference between neural networks containing and not containing feedback between elements is analyzed. It is shown that in the first of these cases, quantitative relationships describing the functioning of the neural network can be obtained based on an analogy with the theory of noise-resistant codes. In the second case, an analogy with electronic circuits that form memory cells (triggers) is valid. It is shown that feedback between elements of even the simplest neural networks can lead to the appearance of multidimensional hysteresis, when, with the same state of inputs, the system can be in several qualitatively different states, the transition between which can be abrupt. In this case, the state of the neural network outputs depends not only on the current state of its inputs, but also on the path along which this state was formed. The results obtained are used for the philosophical substantiation of a new approach to the interpretation of complex systems of various natures, which are considered analogs of neural networks. According to it, a system that can store and processing information should be considered "complex".