V. V. Kolushov – Ph.D. (Eng.), Associate Professor, Department of High Mathematics, Ufa State Aviation Technical University

E-mail: KVV@ufanet.ru

A. V. Savelyev – Ph.D. (Philos.), Patent agency «©Uniquely honest patenting» (Moscow)

E-mail: gmkristo@yandex.ru

In neurocomputing at the current level of its understanding, the ideology of the systems approach is dominated not only by the morphological and hard-plan of its presentation, when a complex system is thought of as assembled from simple elements – “bricks”, whose properties do not reduce the properties of an integral system [1]. The same vicious methodology can also be attributed to existing content, which is well reflected in the programming methodology, when a complex program is composed of simple operators and operands. Unfortunately, this methodology, determined by certain epistemological postulates [2], extends to our ideas about the functioning of living systems, in particular, neural ones. All this leads to the consideration of the construction of the nervous system like a computer from simple standard elements, which were chosen as a neuron more than 100 years ago [3]. However, this methodological approach is not limited to the appropriate construction of neurocomputers' hard-level and computational information technology and the formation of views on the morphological structure of nervous tissue, but also extend to their information components, namely, to the signals through which information is transmitted. According to the machine-technological concepts, the transfer of a quantum of information is carried out by a quantum of matter – by a signal – by an artificially formed quantum, more precisely, by its similarity. In this case, the quantum properties of such signals can be spoken only at its space-time level, that is, in a narrow range. Here, the properties of this signal are as close to quantum as possible; beyond the same range, this cannot be done, so all the work with such signals is carried out precisely in the range of manifestation of its quantum properties. From here it becomes clear the source of simplification, the applicability of which is legitimate only in artificially created systems from this point of view and according to this methodology. Living systems are absolutely not obliged to be designed according to the mentioned methodology and are not designed at all.

The objective of the article is to show, model and put model experiments based on real neurobiological data on the brain, on the high complexity of information interactions in the nervous system, which, unfortunately, are not taken into account until now, but nevertheless play a crucial role in understanding consciousness and brain cognitive processes. If we consider the axon nerve impulse generated by a neuron, it has been established [3, 4] that it has a complex information structure, which is the imprint of a functioning neuron network of neurons in contact with the neuron generating this axon spike. This information structure can also be considered organized like a neural network itself, with which a neuron contacts, but at the information level. It can be seen that the excitation arising from the action of depolarization threshold and above-threshold stimuli spreads to cover approximately the same sections of the membrane. This is indicated by the localization of singular points on graphs of the spike kinetics. The neural processor [5] simulates the principle of noise organization of neuron electrical responses, as well as the dependence of its nature on the conditions of cell activity.

Thus, the work shows the complexity of the information organization [6–8] and the informational nature of neurons and neural networks of them consisting [9–11].

Kolushov V.V., Savelyev A.V. Information hypercomplexity of neurons and neural networks in the new concept of neurocomputing. Neurocomputers. 2020. Vol. 22. No. 4. P. 38–43. DOI: 10.18127/j19998554-202004-06. (in Russian)

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