Neural Information Organizing and Processing -- Neural Machines
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arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866917630641700864 |
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| author | Petrila, Iosif Iulian |
| author_facet | Petrila, Iosif Iulian |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_03676 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Neural Information Organizing and Processing -- Neural Machines Petrila, Iosif Iulian Neural and Evolutionary Computing Computation and Language Machine Learning 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. |
| title | Neural Information Organizing and Processing -- Neural Machines |
| topic | Neural and Evolutionary Computing Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2404.03676 |