Article
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Neural Information Organizing and Processing – Neural Machines
Version 1
: Received: 15 March 2024 / Approved: 18 March 2024 / Online: 18 March 2024 (16:14:14 CET)
How to cite: Petrila, I. I. Neural Information Organizing and Processing – Neural Machines. Preprints 2024, 2024031043. https://doi.org/10.20944/preprints202403.1043.v1 Petrila, I. I. Neural Information Organizing and Processing – Neural Machines. Preprints 2024, 2024031043. https://doi.org/10.20944/preprints202403.1043.v1
Abstract
The informational synthesis of neural structures, processes, parameters and characteristics that allow a unified description and modeling as neural machines of natural and artificial neural systems is presented. The general informational parameters as the global quantitative measure of the neural systems computing potential as absolute and relative neural power were proposed. Neural information organizing and processing follows the way in which nature manages neural information by developing functions, functionalities and circuits related to different internal or peripheral components and also to the whole system through a non-deterministic memorization, fragmentation and aggregation of afferent and efferent information, deep neural information processing representing multiple alternations of fragmentation and aggregation stages. The relevant neural characteristics were integrated into a neural machine type model that incorporates unitary also peripheral or interface components as the central ones. The proposed approach allows overcoming the technical constraints in artificial computational implementations of neural information processes and also provides a more relevant description of natural ones.
Keywords
neural information; neural machines; neural networks; sensors and actuators; nervous systems; artificial intelligence
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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