The economic component of the principles of building synthetic nervous systems

Introduction. The digital economy, which the Russian Federation has embarked on, consists of a set of automated control systems, the effectiveness of each of which determines the effectiveness of the entire digital economy. Therefore, one of the main tasks that needs to be solved on the way to its construction is the creation of a fundamentally new, cost-effective architecture for building automated control systems.

Purpose. This scientific study pursues the fundamental objective of systematizing and precisely formulating the postulates for constructing automated control systems (ACS) based on synthetic nervous systems (SNS). It represents a logical extension of the authors' publication series dedicated to a comprehensive assessment of the economic efficiency of this paradigm in developing data acquisition automation and process control systems. The proclaimed economic advantages, thoroughly analyzed in prior research, are attainable exclusively through strict and unwavering adherence to these postulates; their violation entirely nullifies or substantially diminishes the positive economic effect of SNS implementation.

Methods. The methodological foundation encompasses a systematic and comprehensive analysis of an extensive corpus of technical and scientific literature on ACS development and optimization. Building on this, the authors consolidated the identified principles under the unified designation "SNS construction postulates", providing detailed descriptions of each alongside quantitative evalua￾tions of their economic impacts. The approach leverages simple yet universal engineering practices, which despite their evident nature to specialists have previously lacked clear and consolidated formulation in available publications.

Results. As the key result, postulates are formulated, positioned as fundamental directives and imperatives of design. Thorough assimilation and application of the postulates is declared mandatory for every automation system integration engineer, guaranteeing the achievement of economic efficiency in automation systems. The formulated postulates will not surprise qualified specialists due to their obvious logical simplicity and practical validity. Conclusions. Synthetic nervous systems are a new direction in the development of automated control systems. Russia could become a leader in their production and the birthplace of this technology. It is obvious that the economy of the future is not a digital economy or even a knowledge economy, but, above all, an economy of high, knowledge–intensive technologies. One of the ways the Russian Federation can succeed in the world of the economic future is to find new, more effective methods of using the technologies available to it, even if they are foreign. The development of synthetic nervous system technology is one such way.

1. Abashkin V.L., Abdrahmanova G.I., Vishnevsky K.O., Gokhberg L.M., et al. Indicators of the digital economy: 2025 [Electronic resource]. National Research University Higher School of Economics, Institute for Statistical Studies and Economics of Knowledge. Moscow: HSE ISIEZ; 2025.296 p. ISBN 978-5-7598-3029-0. https://issek.hse.ru/mirror/pubs/share/1026726402.pdf (Accessed December 21, 2025). (In Russ.). https://doi.org/10.17323/978-5-7598-3029-0

2. Bodrova E.V., Kalinov V.V., & Krasivskaya V.N. Development of Soviet computing technology in the 1960s (based on declassified archival documents). Locus: People, Society, Cultures, Meanings. 2024;15(3):68–81. (In Russ.). https://doi.org/10.31862/2500-2988-2024-15-3-68-81. EDN: CDRHLQ

3. Lugachev M.I. Once again on scientific and technological progress and the advantages of socialism. Vestnik of Moscow University. Series 6: Economics. 2021;(6):247–263. (In Russ.). EDN: UKALAJ.

4. Puzanova I.A. Key elements of business digital transformation. Russian Journal of Management. 2023;11(2):160–178. (In Russ.). https://doi.org/10.29039/2409-6024-2023-11-2-160-174. EDN: KXRCJL

5. Zherdin D. A., Dmitriev A. G. Basics of automation based on synthetic nervous systems. Innovations and Investments. 2024;(11). (In Russ.). https://doi.org/10.24412/2307-180X-2024-11-388-393. EDN: QWFLLD

6. Zherdin D. A., Dmitriev A. G. Economics of automation based on synthetic nervous systems. State and Municipal Management. Scholarly Notes. 2025;(1):280–289. (In Russ.). EDN: QXPBMV.

7. Zherdin D. A., Dmitriev A. G. Cost advantages of physical neural networks. Modern Economy Success. 2024;(4):44–54. (In Russ.). https://doi.org/10.58224/2500-3747-2024-4-44-54. EDN: FZWHZM

8. Ripoll-Sánchez, L., Watteyne, J., Sun, H., et al. The neuropeptidergic connectome of C. elegans. Neuron. 2023;111(22):3570-3589.e5. https://doi.org/10.1016/j.neuron.2023.09.043

9. Dupré C., & Yuste R. Non-overlapping neural networks in Hydra vulgaris. Current Biology. 2017;27(8):1085-1097. https://doi.org/10.1016/j.cub.2017.02.049

10. Goulty M., Botton-Amiot G., Rosato E., et al. The monoaminergic system is a bilaterian innovation. Nature Communications. 2023;(14):3284. https://doi.org/10.1038/s41467-023-39030-2. EDN: UZNQWJ

11. Davies M. Benchmarks for progress in neuromorphic computing. Nature Machine Intelligence. 2019;(1):386–388. https://doi.org/10.1038/s42256-019-0097-1. EDN: LFGFMH

12. Liu, X., Lu, Y., Iseri, E., Shi, Y., Kuzum, D., et al. A compact closed-loop optogenetics system based on artifact-free transparent graphene electrodes. Frontiers in Neuroscience. 2018;12(MAR):132. https://doi.org/10.3389/fnins.2018.00132. EDN: YFWJJJ

13. Erohin I.S., Skobeeva V.A., Chernov T.A. Introduction to functional morphology of the nervous system: Methodical materials in biology. Dolgoprudny: MIPT; 2018. 46 p. (In Russ.)

14. Zherdin D.A. Synthetic nervous systems. Introduction to basics [Electronic resource]: Monograph. Novokuznetsk: Znanije-M Publishing; 2025. 204 p. – ISBN 987-5-00255-415-7. (In Russ.). https://doi.org/10.38006/00255-415-7.2025.1.204. EDN: JHMPLR.

15. Azizov B.E. Improving energy efficiency by reducing energy losses in high-voltage transmission networks and long-term operating devices. Economy and Society. 2025;5-1:132. (In Russ.). EDN: QJSAOJ.

16. Perevoznikova T.V., Shlyakhtin G.V. Functional organization of the nervous system: Histology, anatomy, embryogenesis, evolution (interdisciplinary aspects). Part I. Structure, functioning, and embryonic development of nervous tissue: Teaching and methodological manual for biology faculty students. Saratov: Amirit LLC;2021. 97 p. (In Russ.). ISBN 978-5-00140-928-1

17. Dindyaev S.V., Vinogradov S.Yu. Illustrated practicum on private histology of the nervous system and sensory organs [Electronic resource]: Electronic teaching and control manual. Ivanovo State Medical Academy, Department of Histology, Embryology and Cytology. Ivanovo: IvGMA Roszdrav. 2010. (In Russ.).

18. Ortega A., Olivares-Bañuelos T.N. Neurons and glia cells in marine invertebrates: An update. Frontiers in Neuroscience. 2020;14:121. https://doi.org/10.3389/fnins.2020.00121. EDN: DLVFTM

19. Shulman E. Unofficial Windows 95: Developer's guide dedicated to researching the basics of Windows™95 "Chicago"[Trans.]. Kyiv, Moscow: Dialectika Publishing, Information Computer Enterprise. 1995. 455 p. (In Russ.).

20. Dyakonova V.E., Sakharov D.A. Post-reflex neurobiology of behavior. Moscow: YASK Publishing House; 2019. 592 p. (In Russ.). ISBN 978-5-907117-52-5

21. Galanov E.K. Action potential of a neuron with a model membrane. International Journal of Applied and Fundamental Research. 2018;5-2:312–317. (In Russ.). EDN: XUEIVV

22. Karelov A.E. Modern concepts of pain mechanisms. Russian Journal of Anesthesiology and Reanimatology. 2020;(6):88-95. (In Russ.) DOI: 10.17116/anaesthesiology202006188 EDN: IGJGPW

23. Honeycutt S.E., N'Guetta P.Y., O'Brien L.L. Innervation in organogenesis. Current Topics in Developmental Biology. 2022;(148):195–235. https://doi.org/10.1016/bs.ctdb.2022.02.004. EDN: CHBOQE

24. Gebicke-Haerter P.J. The computational power of the human brain. Frontiers in Cellular Neuroscience. 2023;(17):Article 1220030. https://doi.org/10.3389/fncel.2023.1220030. EDN: XQHMBB

25. Krotov V., Agashkov K., Romanenko S., et al. Elucidating afferent-driven presynaptic inhibition of primary afferent input to spinal laminae I and X. Frontiers in Cellular Neuroscience. 2023;(16):Article 1029799. https://doi.org/10.3389/fncel.2022.1029799. EDN: CXONER

26. Lidierth M. Local and diffuse mechanisms of primary afferent depolarization and presynaptic inhibition in the rat spinal cord. Journal of Physiology. 2006. 576(Pt 1):309-327. https://doi.org/10.1113/jphysiol.2006.110577. EDN: XSRBMF

27. Delyagin V.M. Autonomic nervous system and its disorders. Effective Pharmacotherapy. 2024;20(17):22–29. (In Russ.). https://doi.org/10.33978/2307-3586-2024-20-17-22-28. EDN: ZHBKTI

28. Goldstein D., Robertson D., Essler M., et al. Dysautonomias: Clinical disorders of the autonomic nervous system. Annals of Internal Medicine. 2002;137(9):753–763. https://doi.org/10.7326/0003-4819-137-9-200211050-00011

29. Chelimsky Th., Chelimsky G. Disorders of the autonomic nervous system. In: J. Jancovic, J. Mazziotta, S. Pomeroy, et al. (Eds.). Newman Bradley and Daroff's neurology in clinical practice. Elsevier, 2021; 1930-1957.

30. Sidorov A.V. Functional activity of invertebrate nerve centers. Minsk: BSU; 2011. 247 p. (In Russ.). ISBN: 978-985-518-304-5. EDN: FTDOVT

31. Shimizu H. Feeding and wounding responses in Hydra suggest functional and structural polarization of the tentacle nervous system. Comparative Biochemistry and Physiology Part A: Molecular & Integrative Physiology. 2002;131(3):669–674. https://doi.org/10.1016/s1095-6433(01)00516-5

32. Kalinikova T.B., Timoshenko A.Kh., Kolsanova R.R., Zakharov S.V., Gainutdinov M.Kh., Shagidullin R.R. Effect of acetaldehyde on free-living soil nematodes Caenorhabditis elegans strains N2 and IPE1. Toxicological Herald. 2012;4(115):45–48. (In Russ.).

33. Amamchyan A.E., Gafiyatullina G.Sh. Neuroplasticity as the basis of motor rehabilitation. Medical Herald of the South of Russia. 2023;14(4):122–128. (In Russ.). https://doi.org/10.21886/2219-8075-2023-14-4-122-128. EDN: HSHVUJ

34. Bushov Yu.V., Ushakov V.L., Svetlik M.V., Kartashov S.I., Orlov V.A. The role of mirror neurons in interpreting actions and intentions. Tomsk State University Journal of Biology. 2021;56:86–107. (In Russ.). https://doi.org/10.17223/19988591/56/4. EDN: FCQOHZ

35. Goggins E., Mitani S., Tanaka S. Clinical perspectives on vagus nerve stimulation: Present and future. Clinical Science. 2022;136(9):695–709. https://doi.org/10.1042/CS20210507. EDN: SYIPWY

36. Jung S., Pak S., Lee K., Kang C. Classification of human failure in chemical plants: Case study of various types of chemical accidents in South Korea from 2010 to 2017. International Journal of Environmental Research and Public Health. 2021;18(21):11216. https://doi.org/10.3390/ijerph182111216. EDN: TEEZBQ

37. Palazzi E., Currò F., Fabiano B. A critical approach to safety equipment and emergency time evaluation based on actual information from the Bhopal gas tragedy. Process Safety and Environmental Protection. 2015;(97):37–48. https://doi.org/10.1016/j.psep.2015.06.009

38. Medvedev D. L. Creation of the ARPANET network. Electrosvyaz Magazine. 2008;(S1):23–28. (In Russ.). EDN: IUMOWD

39. Gokhberg L. M., Ditkovsky, K. A., Evnevich, E. I., et al. Indicators of science: 2025: Statistical collection. National Research University Higher School of Economics. Moscow: HSE ISIEZ; 2025. 396 p. ISBN 978-5-7598-3032-0. https://issek.hse.ru/mirror/pubs/share/1013106714.pdf (Accessed December 21, 2025). (In Russ.).