{"id":9734,"date":"2026-04-02T12:31:18","date_gmt":"2026-04-02T11:31:18","guid":{"rendered":"https:\/\/izvestiyakbncran.ru\/?page_id=9734"},"modified":"2026-04-06T14:14:16","modified_gmt":"2026-04-06T13:14:16","slug":"25-5-3-en","status":"publish","type":"page","link":"https:\/\/izvestiyakbncran.ru\/index.php\/en\/25-5-3-en\/","title":{"rendered":"25.5.3 En"},"content":{"rendered":"\n<h1 class=\"wp-block-heading has-lora-font-family\" style=\"font-size:22px\"><strong>Energy exchange model between agneurons as part of multi-agent neurocognitive architecture<\/strong><\/h1>\n\n\n\n<p class=\"has-foreground-color has-text-color has-link-color has-lora-font-family has-extra-small-font-size wp-elements-7ed0e240d8b0e07f7c8d71de681567b6\" style=\"margin-top:0;margin-bottom:0;padding-top:0;padding-bottom:0\"><strong>I.A. Pshenokova, A.Z. Apshev<\/strong><\/p>\n\n\n\n<p class=\"has-foreground-color has-text-color has-link-color has-lora-font-family has-extra-small-font-size wp-elements-86b70c892ee51d64e6bf0730e3274f25\" style=\"margin-top:0;margin-bottom:0;padding-top:0;padding-bottom:0\"><\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity is-style-wide\" style=\"margin-top:var(--wp--preset--spacing--20);margin-bottom:var(--wp--preset--spacing--20)\"\/>\n\n\n\n<div class=\"wp-block-group is-nowrap is-layout-flex wp-container-core-group-is-layout-d3328a80 wp-block-group-is-layout-flex\" style=\"margin-top:0;margin-bottom:0;padding-top:var(--wp--preset--spacing--20);padding-bottom:var(--wp--preset--spacing--20)\">\n<p class=\"has-text-color has-link-color has-lora-font-family has-extra-small-font-size wp-elements-fa99f84d8051eb763ab85c3007cdb1c2\" style=\"color:#5b1919;text-decoration:underline\"><strong><strong>Upload the full text<\/strong><\/strong><\/p>\n\n\n\n<div class=\"wp-block-group is-vertical is-layout-flex wp-container-core-group-is-layout-9151b400 wp-block-group-is-layout-flex\" style=\"min-height:0px;margin-top:0;margin-bottom:0;padding-top:0;padding-bottom:0\">\n<div class=\"wp-block-buttons is-content-justification-left is-layout-flex wp-container-core-buttons-is-layout-15bf754d wp-block-buttons-is-layout-flex\" style=\"margin-top:0;margin-bottom:0;padding-top:0;padding-right:0;padding-bottom:0;padding-left:0\">\n<div class=\"wp-block-button has-custom-width wp-block-button__width-100 is-style-outline is-style-outline--1\"><a class=\"wp-block-button__link has-background-background-color has-text-color has-background has-link-color has-border-color has-small-font-size has-custom-font-size wp-element-button\" href=\"http:\/\/izvestiyakbncran.ru\/wp-content\/uploads\/2026\/04\/3-pshe.pdf\" style=\"border-color:#5b1919;border-style:solid;border-width:2px;border-radius:8px;color:#5b1919;padding-top:0.4rem;padding-right:var(--wp--preset--spacing--40);padding-bottom:0.4rem;padding-left:var(--wp--preset--spacing--40)\">PDF<\/a><\/div>\n<\/div>\n\n\n\n<div style=\"height:0px;width:0px\" aria-hidden=\"true\" class=\"wp-block-spacer wp-container-content-273e683f\"><\/div>\n<\/div>\n<\/div>\n\n\n\n<p class=\"has-foreground-color has-text-color has-link-color has-lora-font-family has-extra-small-font-size wp-elements-b456a92906b58ac3f4f553041812ab27\" style=\"line-height:1.4\"><em><strong><strong>Abstract<\/strong>:<\/strong> <\/em>In recent years distributed artificial intelligence has attracted the attention of scientists due to its ability to solve complex computing problems. The main area of this article is multi-agent systems. The flexibility of multi-agent systems makes them suitable for solving problems in various disciplines, including computer science, economics, civil construction, etc. The aim of this study is to build an imitation model of energy exchange between agents in an intellectual decision-making system based on multi-agent neurocognitive architecture. The object of study is the process of energy exchange in the neural structure of the brain. The work proposes a model of energy exchange between agneurons as part of a multi-agent neurocognitive architecture of an intellectual agent. The proposed formalism is based on the neurofunctional similarity of the agneurons of an intellectual agent with neurons of the human brain. The process of energy exchange and consumption of the brain neurons in the process of performing cognitive functions is considered. In particular, the work combines the knowledge gained as a result of the study of mitochondrial function and the metabolic energy of the brain. Formalism is presented for calculating the energy of agneurons and actors at different levels of the invariant of multi-agent neurocognitive architecture of an intelligent agent. Further work will be to test the presented architecture in the simulation modeling program.<\/p>\n\n\n\n<p class=\"has-foreground-color has-text-color has-link-color has-lora-font-family has-extra-small-font-size wp-elements-df03325adc83c022634de6b79d43432f\" style=\"line-height:1.4\"><\/p>\n\n\n\n<p class=\"has-foreground-color has-text-color has-link-color has-lora-font-family has-extra-small-font-size wp-elements-dbc1e8bd45c6c6ebd7fce9c6b5a8ad38\" style=\"line-height:1.4\"><strong><em><strong>Keywords<\/strong>:<\/em><\/strong> intellectual agent, multiagent systems, cognitive architecture, decision making and management systems<\/p>\n\n\n\n<p class=\"has-foreground-color has-text-color has-link-color has-lora-font-family has-extra-small-font-size wp-elements-df03325adc83c022634de6b79d43432f\" style=\"line-height:1.4\"><\/p>\n\n\n\n<p class=\"has-foreground-color has-text-color has-link-color has-lora-font-family wp-elements-808136ebee1bdf183209b2b2b7bf95e7\" style=\"font-size:12px;line-height:1.4\"><strong><strong>For citation<\/strong>.<\/strong> Pshenokova I.A., Apshev A.Z. Energy exchange model between agneurons as part of multi-agent neurocognitive architecture. News of the Kabardino-Balkarian Scientific Center of RAS. 2023. No. 5(115). Pp. 32\u201340. DOI: 10.35330\/1991-6639-2023-5-115-32-40<\/p>\n\n\n\n<p class=\"has-foreground-color has-text-color has-link-color has-lora-font-family wp-elements-17c3333a0b1db39b7fa4a3e1a1572bc4\" style=\"font-size:12px;line-height:1.4\"><\/p>\n\n\n\n<details class=\"wp-block-details has-foreground-color has-text-color has-link-color has-lora-font-family has-extra-small-font-size wp-elements-f3a04537dc2be7739277f92999c6bb27 is-layout-flow wp-container-core-details-is-layout-0ab540ad wp-block-details-is-layout-flow\" style=\"font-style:normal;font-weight:700;line-height:1.5\"><summary><strong>R<\/strong>eferences<\/summary>\n<ol style=\"margin-top:0;margin-bottom:0\" class=\"wp-block-list\">\n<li style=\"font-style:normal;font-weight:400\">Dorri A., Kanhere S., Jurdak R. Multi-agent systems: A Survey. <em>IEEE Access. <\/em>2018. Vol. 6. Pp. 28573\u201328593. DOI: 10.1109\/ACCESS.2018.2831228<\/li>\n\n\n\n<li style=\"font-style:normal;font-weight:400\">Bond A., Gasser L. Readings in distributed artificial intelligence. San Mateo, CA, USA: Morgan Kaufmann, 2014. 668 p.<\/li>\n\n\n\n<li style=\"font-style:normal;font-weight:400\">Wooldridge M. An Introduction to multiagent systems. New York, NY, USA: Wiley, 488 p.<\/li>\n\n\n\n<li style=\"font-style:normal;font-weight:400\">Shamshirband S., Anuar N., Kiah M., Patel A. An appraisal and design of a multi-agent system based cooperative wireless intrusion detection computational intelligence technique. <em>Eng. Appl. Artif. Intell<\/em>. 2013. Vol. 26. Pp. 2105\u20132127. DOI:10.1016\/j.engappai.2013.04.010<\/li>\n\n\n\n<li style=\"font-style:normal;font-weight:400\">Zou A.-M., Kumar K., Hou Z.-G. Distributed consensus control for multi-agent systems using terminal sliding mode and Chebyshev neural networks. Int. J.<em> Robust Nonlinear Control<\/em>. 2013. Vol. 23(3). Pp. 334\u2013357. DOI: 10.1002\/rnc.1829<\/li>\n\n\n\n<li style=\"font-style:normal;font-weight:400\">Calvaresi D. et al. Real-time multi-agent systems: rationality, formal model, and empirical results. <em>Autonomous agents and multi-agent systems<\/em>. 2021. Vol. 35(1). P. 12. DOI: 10.1007\/s10458-020-09492-5.<\/li>\n\n\n\n<li style=\"font-style:normal;font-weight:400\">Zhang D. et al. Physical safety and cyber security analysis of multi-agent systems: A survey of recent advances. <em>IEEE\/CAA Journal of automatica sinica.<\/em> 2021. Vol. 8(2). Pp. 319\u2013333. DOI: 10.1109\/JAS.2021.1003820<\/li>\n\n\n\n<li style=\"font-style:normal;font-weight:400\">Rezaee H., Abdollahi F. Average consensus over high-order multiagent systems. <em>IEEE Trans. autom. control. <\/em>2015. Vol. 60(11). Pp. 3047\u20133052. DOI: 10.1109\/TAC.2015.2408576<\/li>\n\n\n\n<li style=\"font-style:normal;font-weight:400\">Ma L., Min H., Wang S. et al. An overview of research in distributed attitude coordination control. <em>IEEE<\/em>\/<em>CAA J. autom. sinica<\/em>. 2015. Vol. 2(2). Pp. 121\u2013133.<\/li>\n\n\n\n<li style=\"font-style:normal;font-weight:400\">Nagoev Z., Pshenokova I., Nagoeva O. et al. Learning algorithm for an intelligent decision making system based on multi-agent neurocognitive architectures. <em>Cognitive systems research<\/em>. Vol. 66. Pp. 82\u201388. DOI: 10.1016\/j.cogsys.2020.10.015<\/li>\n\n\n\n<li style=\"font-style:normal;font-weight:400\">Nagoev Z.V. <em>Intellektika, ili myshlenie v zhivykh i iskusstvennykh sistemakh<\/em> [Intellectics, or thinking in natural and artificial systems]. Nal&#8217;chik: Izdatel&#8217;stvo KBNTS RAN, 2013. 211 p.<\/li>\n\n\n\n<li style=\"font-style:normal;font-weight:400\">Nagoev Z.V. Multi-agent existential mappings and functions. <em>News of the Kabardino-Balkarian Scientific Center of RAS<\/em>. 2013. No. 4(54). Pp. 63\u201371.<\/li>\n\n\n\n<li style=\"font-style:normal;font-weight:400\">Nagoev Z., Pshenokova I., Nagoeva O. et al. Situational analysis model in an intelligent system based on multi-agent neurocognitive architectures. <em>Journal of Physics: Conference Series. <\/em>2131 (2021) 022103. DOI: 10.1088\/1742-6596\/2131\/2\/022103<\/li>\n\n\n\n<li style=\"font-style:normal;font-weight:400\">Picard M., McEwen B.S. Mitochondria impact brain function and cognition. <em>Proceedings of the National Academy of Sciences<\/em>. 2014. Vol. 111. No. 1. Pp. 7\u20138.<\/li>\n\n\n\n<li style=\"font-style:normal;font-weight:400\">Wallace D.C. Bioenergetics, the origins of complexity, and the ascent of man. <em>Proceedings of the National Academy of Sciences<\/em>. 2010. Vol. 107. No. supplement_2. Pp. 8947\u20138953.<\/li>\n\n\n\n<li style=\"font-style:normal;font-weight:400\">Chan D.C. Fusion and fission: interlinked processes critical for mitochondrial health. <em>Annual Review of genetics<\/em>. 2012. Vol. 46. Pp. 265\u2013287.<\/li>\n\n\n\n<li style=\"font-style:normal;font-weight:400\">Pshenokova I.A., Nagoeva O.V., Apshev A.Z. et al. Formation of dynamic cause-and-effect relationships in controlling the behavior of an intelligent agent based on the formalism of multiagent neurocognitive architectures. News of the Kabardino-Balkarian Scientific Center of RAS. No. 5 (109). Pp. 73\u201380. DOI: 10.35330\/1991-6639-2022-5-109-73-80<\/li>\n\n\n\n<li style=\"font-style:normal;font-weight:400\">Raichle M.E., Gusnard D.A. Appraising the brain&#8217;s energy budget. PNAS. 2002. Vol. 99(16). Pp. 10237\u201310239. DOI: 10.1073\/pnas.172399499<\/li>\n\n\n\n<li style=\"font-style:normal;font-weight:400\">Bruckmaier M., Tachtsidis I., Phan P. e al. Attention and Capacity Limits in Perception: A Cellular Metabolism Account. Journal of Neuroscience. 2020. Vol. 40 (35). Pp. 6801\u20136811. DOI: 10.1523\/JNEUROSCI.2368-19.2020<\/li>\n<\/ol>\n<\/details>\n\n\n\n<details class=\"wp-block-details has-foreground-color has-text-color has-link-color has-lora-font-family has-extra-small-font-size wp-elements-96e99132c034257d015e4b648b8d322b is-layout-flow wp-container-core-details-is-layout-5dafc681 wp-block-details-is-layout-flow\" style=\"font-style:normal;font-weight:700;line-height:1.5\"><summary><strong>Information about the authors<\/strong><\/summary>\n<div class=\"wp-block-group is-vertical is-layout-flex wp-container-core-group-is-layout-b291ae12 wp-block-group-is-layout-flex\" style=\"min-height:0px;margin-top:0;margin-bottom:0;padding-top:var(--wp--preset--spacing--20);padding-right:var(--wp--preset--spacing--40);padding-bottom:var(--wp--preset--spacing--20);padding-left:var(--wp--preset--spacing--40)\">\n<p style=\"font-style:normal;font-weight:400\"><strong>Pshenokova Inna Auesovna<\/strong>, Candidate of Physical and Mathematical Sciences, Head of lab., Institute of Computer Science and Problems of Regional Management \u2013 branch of Kabardino-Balkarian Scientific Center of the Russian Academy of Sciences;<br>360000, Russia, Nalchik, 37-a I. Armand street;<br>pshenokova_inna@mail.ru, ORCID: https:\/\/orcid.org\/0000-0003-3394-7682<br><strong>Apshev Artur Zaurbievich<\/strong>, Research Assistant, the Institute of Computer Science and Problems of Regional Management \u2013 branch of Kabardino-Balkarian Scientific Center of the Russian Academy of Sciences;<br>360000, Russia, Nalchik, 37-a I. Armand street;<br>apshev@mail.ru<\/p>\n\n\n\n<p style=\"font-style:normal;font-weight:400\"><\/p>\n\n\n\n<p style=\"font-style:normal;font-weight:400\"><\/p>\n\n\n\n<p style=\"font-style:normal;font-weight:400\"><\/p>\n\n\n\n<p style=\"font-style:normal;font-weight:400\"><\/p>\n\n\n\n<p style=\"font-style:normal;font-weight:400\"><\/p>\n<\/div>\n<\/details>\n","protected":false},"excerpt":{"rendered":"<p>Energy exchange model between agneurons as part of multi-agent neurocognitive architecture I.A. Pshenokova, A.Z. Apshev Upload the full text Abstract: In recent years distributed artificial intelligence has attracted the attention of scientists due to its ability to solve complex computing problems. The main area of this article is multi-agent systems. The flexibility of multi-agent systems [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"wp-custom-template-home","meta":{"footnotes":""},"class_list":["post-9734","page","type-page","status-publish","hentry"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.5 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>25.5.3 En - \u0418\u0417\u0412\u0415\u0421\u0422\u0418\u042f \u041a\u0410\u0411\u0410\u0420\u0414\u0418\u041d\u041e-\u0411\u0410\u041b\u041a\u0410\u0420\u0421\u041a\u041e\u0413\u041e \u041d\u0410\u0423\u0427\u041d\u041e\u0413\u041e \u0426\u0415\u041d\u0422\u0420\u0410 \u0420\u0410\u041d\u00bb<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/izvestiyakbncran.ru\/index.php\/en\/25-5-3-en\/\" \/>\n<meta property=\"og:locale\" content=\"ru_RU\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"25.5.3 En - \u0418\u0417\u0412\u0415\u0421\u0422\u0418\u042f \u041a\u0410\u0411\u0410\u0420\u0414\u0418\u041d\u041e-\u0411\u0410\u041b\u041a\u0410\u0420\u0421\u041a\u041e\u0413\u041e \u041d\u0410\u0423\u0427\u041d\u041e\u0413\u041e \u0426\u0415\u041d\u0422\u0420\u0410 \u0420\u0410\u041d\u00bb\" \/>\n<meta property=\"og:description\" content=\"Energy exchange model between agneurons as part of multi-agent neurocognitive architecture I.A. Pshenokova, A.Z. Apshev Upload the full text Abstract: In recent years distributed artificial intelligence has attracted the attention of scientists due to its ability to solve complex computing problems. The main area of this article is multi-agent systems. 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Pshenokova, A.Z. Apshev Upload the full text Abstract: In recent years distributed artificial intelligence has attracted the attention of scientists due to its ability to solve complex computing problems. The main area of this article is multi-agent systems. 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