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Spike-collective neural computing visual construction super complex neuronal system


V.V. Kolushov, A.V. Savelyev

In the paper the substantiation of the possibility and necessity of another order of information content in the recorded empirical data was making. This allows us apart from the traditional Data mining to produce not only the direct receipt of the findings from the data, extract knowledge, but along with it and produce their active construction of the initial data, one way or another, contained in the empirical data. Propose another alternative way to build (construction) of non-analytic systems using expert data received by full-scale facility as the primary and the continuation of their heuristic with obtaining a numerical and graphical-shaped new data with the prediction of the systems behavior. The description of the work developed by the authors of algorithms and programs for 4D-visualization and animation of axons is resulted. Algorithms that are based on these models allow us to represent the spike as a collective object of cumulative interacting microcomponents from different sites (channel groups domain) of neural membrane. This makes it possible not only to improve the accuracy of simulation, but also to retrieve new data, namely, to observe previously unknown effects. The methodology developed by the authors, specific to a super-constructed simulated living facility, may be general enough to apply to any tissue. This methodology is a conceptual and can be used as a general approach for modeling highly complex systems of space-time and functional dynamics, including morphological, as well as the expansion of the concept of paradigm neurocomputer.
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May 29, 2020

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