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Functional geometry of dendrites and its simulating


V.V. Kolushov – Ph.D. (Eng.), Associate Professor, Department of «Нigher mathematics», Ufa State Aviation Technical University
A.V. Savelyev – Ph.D. (Philos.), Senior Research Scientist, Director of the Patent agency «©Uniquely honest patenting»

The paper shows how great importance is attached to the real complex geometry of dendritic fibers on the results of modeling of neural activity, which makes it possible to conclude that it is necessary to represent a neuron as an electromechanical element with a functional choreography of dendrites. The potential distribution at the outputs of the dendritic modeling blocks also showed the possibility of the appearance of potential artifacts, for example, in the form of emissions at certain values of the structural parameters of the dendrite geometry [5]. Since nature does not tolerate "emptiness" ("fullness"), then forces are created that maximize the entropy by equalizing the energy parameters. In the case of such operation of the synaptic inputs, when in this complex configuration of the dendrite, potential artefacts in the form of overvoltages appear on its membrane, eliminating them (alignment) is possible only by changing the structural parameters of the dendrite, for example, by rotating it. Thus, for the first time the existence of functional mobility of dendrites of neurons under certain states of synaptic flows of incoming spikes is theoretically substantiated [6]. Thus, it is shown that the vital activity of a neuron is not reduced only to electrical activity, but is governed by its interaction with the mechanical activity of a neuron, and the neuron itself is an electromechanical element. A key approach to research this is the analysis and modeling of complex geometry of the real neuron dendrite.
This work was financially supported by RHSF in grant № 15-03-00519а «Post-non-classic paradigm of artificial intellect».

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  6. Bryancev I.S., Kolushov V.V., Savel'ev A.V. Kod polozheniya nejrona kak vozmozhnyj material'nyj substrat kodov soznaniya // Nejrokomp'yutery: razrabotka, primenenie. 2016. № 12. S. 51–58.

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