Standard

Brain Principles Programming. / Vityaev, Evgenii; Kolonin, Anton; Kurpatov, Andrey et al.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Science and Business Media Deutschland GmbH, 2023. p. 424-433 41 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13539 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Chapter › Research › peer-review

Harvard

Vityaev, E, Kolonin, A, Kurpatov, A & Molchanov, A 2023, Brain Principles Programming. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)., 41, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13539 LNAI, Springer Science and Business Media Deutschland GmbH, pp. 424-433. https://doi.org/10.1007/978-3-031-19907-3_41

APA

Vityaev, E., Kolonin, A., Kurpatov, A., & Molchanov, A. (2023). Brain Principles Programming. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 424-433). [41] (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13539 LNAI). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-19907-3_41

Vancouver

Vityaev E, Kolonin A, Kurpatov A, Molchanov A. Brain Principles Programming. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Science and Business Media Deutschland GmbH. 2023. p. 424-433. 41. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-19907-3_41

Author

Vityaev, Evgenii ; Kolonin, Anton ; Kurpatov, Andrey et al. / Brain Principles Programming. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Science and Business Media Deutschland GmbH, 2023. pp. 424-433 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

BibTeX

@inbook{ab67e1cd1f7f4f2cb6187aac371b7581,
title = "Brain Principles Programming",
abstract = "The monograph “Strong Artificial Intelligence. On the Approaches to Superintelligence”, referenced by this paper, provides a cross-disciplinary review of Artificial General Intelligence (AGI). As an anthropomorphic direction of research, it considers Brain Principles Programming (BPP) – the formalization of universal mechanisms (principles) of the brain{\textquoteright}s work with information, which are implemented at all levels of the organization of nervous tissue. This monograph provides a formalization of these principles in terms of the category theory. However, this formalization is not enough to develop algorithms for working with this information. In the paper, for the description and modeling of BPP, it is proposed to apply mathematical models and algorithms developed by us earlier that model cognitive functions, which are based on well-known physiological, psychological and other natural science theories. The paper uses mathematical models and algorithms of the following theories: P.K.Anokhin{\textquoteright}s Theory of Functional Brain Systems, Eleonor Rosh{\textquoteright}s prototypical categorization theory, Bob Rehter{\textquoteright}s theory of causal models and “natural” classification. As a result, the formalization of the BPP is obtained and computer examples are given that demonstrate the algorithm{\textquoteright}s operation.",
keywords = "Brain principles, Categorization, Category theory, Formal concepts",
author = "Evgenii Vityaev and Anton Kolonin and Andrey Kurpatov and Artem Molchanov",
year = "2023",
doi = "10.1007/978-3-031-19907-3_41",
language = "English",
isbn = "9783031199066",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "424--433",
booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
address = "Germany",

}

RIS

TY - CHAP

T1 - Brain Principles Programming

AU - Vityaev, Evgenii

AU - Kolonin, Anton

AU - Kurpatov, Andrey

AU - Molchanov, Artem

PY - 2023

Y1 - 2023

N2 - The monograph “Strong Artificial Intelligence. On the Approaches to Superintelligence”, referenced by this paper, provides a cross-disciplinary review of Artificial General Intelligence (AGI). As an anthropomorphic direction of research, it considers Brain Principles Programming (BPP) – the formalization of universal mechanisms (principles) of the brain’s work with information, which are implemented at all levels of the organization of nervous tissue. This monograph provides a formalization of these principles in terms of the category theory. However, this formalization is not enough to develop algorithms for working with this information. In the paper, for the description and modeling of BPP, it is proposed to apply mathematical models and algorithms developed by us earlier that model cognitive functions, which are based on well-known physiological, psychological and other natural science theories. The paper uses mathematical models and algorithms of the following theories: P.K.Anokhin’s Theory of Functional Brain Systems, Eleonor Rosh’s prototypical categorization theory, Bob Rehter’s theory of causal models and “natural” classification. As a result, the formalization of the BPP is obtained and computer examples are given that demonstrate the algorithm’s operation.

AB - The monograph “Strong Artificial Intelligence. On the Approaches to Superintelligence”, referenced by this paper, provides a cross-disciplinary review of Artificial General Intelligence (AGI). As an anthropomorphic direction of research, it considers Brain Principles Programming (BPP) – the formalization of universal mechanisms (principles) of the brain’s work with information, which are implemented at all levels of the organization of nervous tissue. This monograph provides a formalization of these principles in terms of the category theory. However, this formalization is not enough to develop algorithms for working with this information. In the paper, for the description and modeling of BPP, it is proposed to apply mathematical models and algorithms developed by us earlier that model cognitive functions, which are based on well-known physiological, psychological and other natural science theories. The paper uses mathematical models and algorithms of the following theories: P.K.Anokhin’s Theory of Functional Brain Systems, Eleonor Rosh’s prototypical categorization theory, Bob Rehter’s theory of causal models and “natural” classification. As a result, the formalization of the BPP is obtained and computer examples are given that demonstrate the algorithm’s operation.

KW - Brain principles

KW - Categorization

KW - Category theory

KW - Formal concepts

UR - https://www.scopus.com/record/display.uri?eid=2-s2.0-85148688681&origin=inward&txGid=017ca3106c5c7ee0b0d21cdee1d57dbb

UR - https://www.mendeley.com/catalogue/3ec9c711-5737-3000-a7b1-fa281adf6527/

U2 - 10.1007/978-3-031-19907-3_41

DO - 10.1007/978-3-031-19907-3_41

M3 - Chapter

SN - 9783031199066

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 424

EP - 433

BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

PB - Springer Science and Business Media Deutschland GmbH

ER -

ID: 56392380