Skip to Main Content

Modelling and Data Analysis
2025. Vol. 15, no. 3, 94–112
doi:10.17759/mda.2025150306
ISSN: 2219-3758 / 2311-9454 (online)

Optimization of signal transmission between neural populations by stimulation of driver nodes

 
Audio is AI-generated
24

, ,

PDF (RUS)
Online translation in english
GS
Information
in Google Scholar
Metrics

Abstract

Context and relevance. Synchronized activity is critical in brain neural networks, yet the precise mechanisms that ensure coherence of activity between different brain parts remain unclear. Objective. To determine how the choice of driver nodes (according to different centrality metrics) affects the propagation of spiking activity between two sparsely connected clusters. Hypothesis. The selection of control nodes in the first cluster improves the propagation of spike activity to the second cluster compared to a random selection of neurons. Methods and Materials. In a two-cluster network modeled by a stochastic block model, a fraction of neurons in the first cluster (10-20%) were subjected to external stimulation. Driver nodes were selected randomly or by centrality: betweenness, closeness, degree, eigenvector, harmonic, and percolation. Results. When stimulating the neurons chosen on the base of various centrality measures, the average firing rate in the second cluster increased proportionally to the number of driver nodes and synchronized with the first cluster. Conclusions. Network topology and driver node selection metrics determine the efficiency and robustness of inter-cluster synchronization, which is important for neuromodulation and the development of bio-inspired computing systems.

General Information

Keywords: spiking neural networks, centrality, synchronization, driver nodes, stochastic block model

Journal rubric: Optimization Methods

Article type: scientific article

DOI: https://doi.org/10.17759/mda.2025150306

Funding. The study was supported by the Russian Science Foundation, project number 24-21-00470, https://rscf.ru/en/project/24-21-00470/.

Received 30.06.2025

Revised 11.07.2025

Accepted

Published

For citation: Batuev, B.B., Onuchin, A.A., Sukhov, S.V. (2025). Optimization of signal transmission between neural populations by stimulation of driver nodes. Modelling and Data Analysis, 15(3), 94–112. (In Russ.). https://doi.org/10.17759/mda.2025150306

© Batuev B.B., Onuchin A.A., Sukhov S.V., 2025

License: CC BY-NC 4.0

References

  1. Fell, J., Axmacher, N. (2011). The role of phase synchronization in memory processes. Nature Reviews. Neuroscience, 12(2), 105–118. https://doi.org/10.1038/nrn2979
  2. Ward, L. M. (2003). Synchronous neural oscillations and cognitive processes. Trends in Cognitive Sciences, 7(12), 553–559. https://doi.org/10.1016/j.tics.2003.10.012
  3. Uhlhaas, P. J., Singer, W. (2006). Neural synchrony in brain disorders: relevance for cognitive dysfunctions and pathophysiology. Neuron, 52(1), 155–168. https://doi.org/10.1016/j.neuron.2006.09.020
  4. Bayati, M., Valizadeh, A., Abbassian, A., Cheng, S. (2015). Self-organization of synchronous activity propagation in neuronal networks driven by local excitation. Frontiers in Computational Neuroscience, 9, 69. https://doi.org/10.3389/fncom.2015.00069
  5. Fletcher, J. M., Wennekers, T. (2018). From Structure to Activity: Using Centrality Measures to Predict Neuronal Activity. International Journal of Neural Systems, 28(2), 1750013. https://doi.org/10.1142/S0129065717500137
  6. Liu, Y.-Y., Slotine, J.-J., Barabási, A.-L. (2011). Controllability of complex networks. Nature, 473(7346), 167–173. https://doi.org/10.1038/nature10011
  7. Gu, S., Pasqualetti, F., Cieslak, M., Telesford, Q. K., Yu, A. B., Kahn, A. E., Medaglia, J. D., Vettel, J. M., Miller, M. B., Grafton, S. T., Bassett, D. S. (2015). Controllability of structural brain networks. Nature Communications, 6(1), 8414. https://doi.org/10.1038/ncomms9414
  8. Dörfler, F., Chertkov, M., Bullo, F. (2013). Synchronization in complex oscillator networks and smart grids. Proceedings of the National Academy of Sciences of the United States of America, 110(6), 2005–2010. https://doi.org/10.1073/pnas.1212134110
  9. Holland, P. W., Laskey, K. B., Leinhardt, S. (1983). Stochastic blockmodels: First steps. Social Networks, 5(2), 109–137. https://doi.org/10.1016/0378-8733(83)90021-7
  10. Boccaletti, S., Latora, V., Moreno, Y., Chavez, M., Hwang, D. (2006). Complex networks: Structure and dynamics. Physics Reports, 424(4-5), 175–308. https://doi.org/10.1016/j.physrep.2005.10.009
  11. Bovet, A., Makse, H. A. (2021). Centralities in complex networks. В Encyclopedia of Complexity and Systems Science (сс. 1–11). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-27737-5_765-1
  12. Abbott, L. F. (1999). Lapicque’s introduction of the integrate-and-fire model neuron (1907). Brain Research Bulletin, 50(5-6), 303–304. https://doi.org/10.1016/s0361-9230(99)00161-6
  13. Stimberg, M., Brette, R., Goodman, D. F. (2019). Brian 2, an intuitive and efficient neural simulator. eLife, 8. https://doi.org/10.7554/eLife.47314
  14. Miller, S., Childers, D. (2012). Probability and Random Processes: With Applications to Signal Processing and Communications. San Diego: Academic Press. https://doi.org/10.1016/C2010-0-67611-5
  15. Fornito, A., Zalesky, A., Bullmore, E. (2016). Fundamentals of brain network analysis. New York: Academic press.

Information About the Authors

Bulat B. Batuev, V.A. Kotelnikov Institute of Radio Engineering and Electronics of the Russian Academy of Sciences (V.A. Kotelnikov IRE RAS), Moscow, Russian Federation, ORCID: https://orcid.org/0000-0002-9322-2635, e-mail: buligarmouth@gmail.com

Arseny A. Onuchin, Junior Researcher, Skolkovo Institute of Science and Technology, Moscow, Russian Federation, ORCID: https://orcid.org/0000-0002-7811-5831, e-mail: arseniyonuchin04.09.97@gmail.com

Sergey V. Sukhov, Candidate of Science (Physics and Matematics), Senior Researcher, Ulyanovsk branch of the Kotelnikov Institute of Radio Engineering and Electronics of the Russian Academy of Sciences (Ulyanovsk branch of the Kotelnikov Institute of Radio Engineering and Electronics of the Russian Academy of Sciences), Ulyanovsk, Russian Federation, ORCID: https://orcid.org/0000-0002-8966-6030, e-mail: ssukhov@ulireran.ru

Contribution of the authors

Sukhov S.V. — ideas; writing and design of the manuscript; planning of the research; control over the research.

Batuev B.B. — application of statistical, mathematical or other methods for data analysis; conducting the experiment; data collection and analysis; visualization of research results, writing and design of the manuscript.

Onuchin A.A. — application of statistical, mathematical or other methods for data analysis; conducting the experiment; data collection and analysis; visualization of research results, writing and design of the manuscript.

All authors participated in the discussion of the results and approved the final text of the manuscript.

Conflict of interest

The authors declare no conflict of interest.

Metrics

 Web Views

Whole time: 92
Previous month: 36
Current month: 7

 PDF Downloads

Whole time: 24
Previous month: 5
Current month: 0

 Total

Whole time: 116
Previous month: 41
Current month: 7