Science for Education Today, 2024, vol. 14, no. 3, pp. 89–112
© Trofimov V. M., 2024
UDC: 
612.82+37.015.31+808.5+316.77

Sustainable dynamics of neural connections: A new concept of the emergence of cognition

Trofimov V. M. 1 (Krasnodar, Russian Federation)
1 Kuban State Technological University
Full text (Open Access) — Downloaded 78
Abstract: 

Introduction. The problem of describing cognition as a result of the biological evolution of neural processes in the brain is especially difficult due to the need to involve a whole range of sciences and the competencies accumulated in them. The aim of this work is to identify and substantiate such dynamics of interaction processes in the neural network of the brain which explains their high intensity and maximum stability in the band of physical limitations of the existence of protein bodies. An important aspect of this goal is the need to substantiate the stages of biological evolution leading to the emergence of cognition (mind).
Materials and Methods. The work mainly used heuristic methods: analogy, hypothetical-deductive method, modeling and thought experiment. The analogy involved the exact results of the theory of turbulence obtained from the variation principle. The modeling used the ideas of the similarity and dimensionality method, as well as the hydrodynamic laminar-turbulent transition. The hypothetical-deductive method used the ideas of the evolutionary method of the origin of species.
Results. The author formulated and substantiated the concept of dynamics of high-intensity and maximally sustainable processes of interaction of neurons of the brain. The main results include the following: the analogy is revealed between information processes in living and inanimate nature with a reasonable common key for their understanding; within the framework of hydrodynamic analogy, a continuum model of the environment of neural interactions is proposed for the first time and the stages of the evolution of the nervous network are substantiated; the hypothesis of the transition to cognition as a consequence of the biological evolution of the neural network is formulated.
Conclusions. The principle of sustainable dynamics allows us to consider cognitive processes from elementary acts of cognition to the emergence of reflection as a whole as an act of consciousness from a single point of view.

Keywords: 

Emergence of cognition; Concept; Connectome; Neural connections; Sustainability; Dynamics of processes; Biological evolution; Consciousness.

For citation:
Trofimov V. M. Sustainable dynamics of neural connections: A new concept of the emergence of cognition. Science for Education Today, 2024, vol. 14, no. 3, pp. 89–112. DOI: http://dx.doi.org/10.15293/2658-6762.2403.05
References: 
  1. Betzel R. F., Faskowitz J., Sporns O. Living on the edge: Network neuroscience beyond nodes. Trends in Cognitive Sciences, 2023, vol. 27 (11), pp. 1068–1084. DOI: https://doi.org/10.1016/j.tics.2023.08.009
  2. Chen H., Yang A., Huang W., Du L., Liu B., Lv K., Luan J., Hu P., Shmuel A., Shu N., Ma G. Associations of quantitative susceptibility mapping with cortical atrophy and brain connectome in Alzheimer's disease: A multi-parametric study. NeuroImage, 2024, vol. 290, pp. 120555. DOI: https://doi.org/10.1016/j.neuroimage.2024.120555
  3. Ciarrusta J., Christiaens D., Fitzgibbon S. P., Dimitrova R., Hutter J., Hughes E., Duff E., Price A. N., Cordero-Grande L., Tournier J.-D., Rueckert D., Hajnal J. V., Arichi T., McAlonan G., Edwards A. D., Dafnis Batalle D. The developing brain structural and functional connectome fingerprint. Developmental Cognitive Neuroscience, 2022, vol. 55, pp. 101117. DOI:  https://doi.org/10.1016/j.dcn.2022.101117
  4. Delgado-García J. M. Cajal and the conceptual weakness of neural sciences. Frontiers in Neuroanatomy, 2015, vol. 9, pp. 128. DOI: https://doi.org/10.3389/fnana.2015.00128     
  5. Ghosh S., Raj A., Nagarajan S. S. A joint subspace mapping between structural and functional brain connectomes. NeuroImage, 2023, vol. 272, pp. 119975. DOI:  https://doi.org/10.1016/j.neuroimage.2023.119975 
  6. Giusti C., Ghrist R., Bassett D. S. Two’s company, three (or more) is a simplex. Journal of Computational Neuroscience, 2016, vol. 41 (1), pp. 1–14. DOI: https://doi.org/10.1007/s10827-016-0608-6
  7. HongY., Cornea E., Girault J. B., Bagonis M., Foster M., Kim S. H., Prieto J. C., Chen H., Gao W., Styner M. A., Gilmore J. H. Structural and functional connectome relationships in early childhood. Developmental Cognitive Neuroscience, 2023, vol. 64, pp. 101314. DOI: https://doi.org/10.1016/j.dcn.2023.101314
  8. Kolk S. M., Rakic P. Development of prefrontal cortex. Neuropsychopharmacology, 2022, vol. 47 (1), pp. 41–57. DOI: https://doi.org/10.1038/s41386-021-01137-9
  9. Kupfermann I. V., Castellucci H., Pinsker H., Kandel E. R. Neuronal correlates of habituation and dishabituation of the gill-withdrawalreflex in Aplysia. Science, 1970, vol. 167, pp. 1743–1745. DOI: https://doi.org/10.1126/science.167.3926.1743  URL: https://www.science.org/doi/10.1126/science.167.3926.1743

10.Li Q., Yue J., Sun J., Chen S., Liu S., Li Z., Xin Y., Hu T. Frontier Development and Insights of International Educational Science Research in the journals Nature and Science: A Systematic Literature Review over 40 Years. Science & Education, 2024, pp. 1–29. DOI:   https://doi.org/10.1007/s11191-024-00509-z

11.Mansour L. S., Di Biase M. A., Smith R. E., Zalesky A., Seguin C. Connectomes for 40,000 UK Biobank participants: A multi-modal, multi-scale brain network resource. NeuroImage, 2023, vol. 283, pp. 120407. DOI: https://doi.org/10.1016/j.neuroimage.2023.120407

12.Moreno-López B., De La Cruz R. R., Pastor A. M., Delgado-García J. M. Effects of botulinum neurotoxin type A on abducens motoneurons in the cat: Ultrastructural and synaptic alterations. Neuroscience, 1997, vol. 81 (2), pp. 457–478. DOI:  https://doi.org/10.1016/S0306-4522(97)00199-1  

13.Pinsker H., Kupfermann I. V., Castellucci H., Kandel E. R. Habituation and dishabituation of the gill-withdrawal in Aplysia. Science, 1970, vol. 167, pp. 1740–1743. DOI: https://doi.org/10.1126/science.167.3926.1740  URL: https://www.science.org/doi/10.1126/science.167.3926.1740

14.Sporns O., Betzel R. F. Modular brain networks. Annual Review of Psychology, 2016, vol. 67 (1), pp. 613–640. DOI: https://doi.org/10.1146/annurev-psych-122414-033634

15.Vogel J. W., Corriveau-Lecavalier N., Franzmeier N., Pereira J. B., Brown J.A., Maass A., Botha H., Seeley W. W., Bassett D. S., Jones D. T., Ewers M. Connectome-based modelling of neurodegenerative diseases: Towards precision medicine and mechanistic insight. Nature Reviews Neuroscience, 2023, vol. 24 (10), pp. 620–639. DOI: https://doi.org/10.1038/s41583-023-00731-8

16.Wang J., He Y. Toward individualized connectomes of brain morphology. Trends in Neurosciences, 2024, vol. 47 (2), pp. 106–119. DOI: https://doi.org/10.1016/j.tins.2023.11.011

17.Wehrheim M. H., Faskowitz J., Sporns O., Fiebach C. J., Kaschube M., Hilger K. Few temporally distributed brain connectivity states predict human cognitive abilities. NeuroImage, 2023, vol. 277, pp. 120246. DOI: https://doi.org/10.1016/j.neuroimage.2023.120246.

18.Yang R., Vishwanathan A., Wu J., Kemnitz N., Ih D., Turner N., Lee K., Tartavull I., Silversmith W. M., Jordan C. S., David C., Bland D., Sterling A., Goldman M. S., Aksay E. R. F., Seung H. S. Cyclic structure with cellular precision in a vertebrate sensorimotor neural circuit. Current Biology, 2023, vol. 33 (11), pp. 2340–2349. DOI: https://doi.org/10.1016/j.cub.2023.05.010 URL: https://www.sciencedirect.com/science/article/pii/S0960982223006048

19.Anokhin K. V. Cognitom: In search of fundamental neuroscientific theory of consciousness. Journal of Higher Nervous Activity, 2021, vol. 71 (1), pp. 39–71.  (In Russian) DOI: https://doi.org/10.31857/S0044467721010032  URL:  https://elibrary.ru/item.asp?doi=10.31857/S0044467721010032

20.Ashilova M. S., Begalinov A. S., Pushkarev Y. V., Begalinova K. K., Pushkareva E. A. Transformation of the higher education system in the context of digital change: A research review on trends in the development of a post-digital university. Science for Education Today, 2023, vol. 13 (6), pp. 99–119. DOI: http://dx.doi.org/10.15293/2658-6762.2306.05  URL: http://en.sciforedu.ru/article/6043 URL: https://elibrary.ru/item.asp?id=58731699

21.Trofimov V. M. The topology of human reflection: Comparison with finite automata. Science for Education Today, 2019, vol. 9 (5), pp. 110–124. (In Russian) DOI: http://dx.doi.org/10.15293/2658-6762.1905.07 URL: https://elibrary.ru/item.asp?id=41271743

22.Trofimov V. M. On the nature of the sustainability of the process in time. Science for Education Today, 2021, vol. 11 (5), pp. 27–42. (In Russian) DOI: http://dx.doi.org/10.15293/2658-6762.2105.02 URL:  https://elibrary.ru/item.asp?id=47136057

23.Chernigovskaya T. V. Biology, environment and culture: From animal communication to human language and cognition. Vestnik of Saint Petersburg University. Philosophy and Conflict Studies, 2020, vol. 36 (1), pp. 157–170.  (In Russian) DOI: https://doi.org/10.21638/spbu17.2020.113 URL: https://elibrary.ru/item.asp?id=42906886

Date of the publication 30.06.2024