Izvestiya of Saratov University.

Physics

ISSN 1817-3020 (Print)
ISSN 2542-193X (Online)

For citation:

Stasenko S. V. Burst dynamics of a spiking neural network caused by the activity of the extracellular matrix of the brain. Izvestiya of Saratov University. Physics , 2024, vol. 24, iss. 2, pp. 138-149. DOI: 10.18500/1817-3020-2024-24-2-138-149, EDN: EOSDSZ

This is an open access article distributed under the terms of Creative Commons Attribution 4.0 International License (CC-BY 4.0).
Published online: 
28.06.2024
Full text:
download
(downloads: 113)
Language: 
Russian
Heading: 
Biophysics and medical physics
Article type: 
Article
UDC: 
530.182
DOI: 
10.18500/1817-3020-2024-24-2-138-149
EDN: 
EOSDSZ

Burst dynamics of a spiking neural network caused by the activity of the extracellular matrix of the brain

Autors: 
Stasenko Sergey Victorovich, Lobachevsky State University of Nizhny Novgorod
Abstract: 

Background and Objectives: The purpose of this work is to study the influence of the extracellular matrix of the brain on the formation of burst dynamics of a spiking neural network. Materials and Methods: The Izhikevich neuron model was used as a neuron model. To describe the dynamics of the extracellular matrix of the brain, the phenomenological model of Kazantsev, constructed using the formalism of the Hodgkin – Huxley model, was used. A model of the formation of burst dynamics of a spiking neural network under the influence of the extracellular matrix of the brain was developed and studied. Results: The main dynamic modes of neural activity have been obtained in the absence of regulation and in the presence of the extracellular matrix of the brain. Conclusion: It has been explored how the modulation by the extracellular matrix of the brain can influence the frequency of burst activity of the neural network. It has been found that the regulation of neural activity, mediated by the extracellular matrix of the brain, promotes the grouping of spikes into quasi-synchronous population discharges, called population bursts. In this case, an increase in the strength of the influence of the extracellular matrix of the brain on postsynaptic currents through synaptic scaling leads to an increase in the degree of synchrony of neuron populations.

Key words: 
spiking neural network
tetrapartite synapse
burst dynamics
Neuron
extracellular matrix of the brain
Acknowledgments: 
This work was supported by the Development Program of the Regional Scientific and Educational Mathematical Center “Mathematics of Future Technologies” (Agreement No. 075-02-2024-1439).
Reference: 
  1. Fell J., Axmacher N. The role of phase synchronization in memory processes. Nature Reviews Neuroscience, 2011, vol. 12, iss. 2, pp. 105–118. https://doi.org/10.1038/nrn2979
  2. Baldauf D., Desimone R. Neural mechanisms of object-based attention. Science, 2014, vol. 344, iss. 6182, pp. 424–427. https://doi.org/10.1126/science.1247003
  3. Timofeev I., Bazhenov M., Seigneur J., Sejnowski T. Neuronal synchronization and thalamocortical rhythms in sleep, wake and epilepsy. In: Jasper’s basic mechanisms of the epilepsies [Internet]. 4th ed. Bethesda (MD), National Center for Biotechnology Information (US), 2012. https://doi.org/10.1093/med/9780199746545.001.0001
  4. Fries P., Reynolds J., Rorie A., Desimone R. Modulation of oscillatory neuronal synchronization by selective visual attention. Science, 2001, vol. 291, iss. 5508, pp. 1560–1563. https://doi.org/10.1126/science.1055465
  5. Wagenaar D., Pine J., Potter S. An extremely rich repertoire of bursting patterns during the development of cortical cultures. BMC Neuroscience, 2006, vol. 7, article no. 11. https://doi.org/10.1186/1471-2202-7-11
  6. Wagenaar D., Nadasdy Z., Potter S. Persistent dynamic attractors in activity patterns of cultured neuronal networks. Physical Review E, 2006, vol. 73, iss. 5, pt. 1, article no. 051907. https://doi.org/10.1103/physreve.73.051907
  7. Zeldenrust F., Wadman W., Englitz B. Neural coding with bursts-current state and future perspectives. Frontiers in Computational Neuroscience, 2018, vol. 12, article no. 48. https://doi.org/10.3389/fncom.2018.00048
  8. Pimashkin A., Kastalskiy I., Simonov A., Koryagina E., Mukhina I., Kazantsev V. Spiking signatures of spontaneous activity bursts in hippocampal cultures. Frontiers in Computational Neuroscience, 2011, vol. 5, article no. 46. https://doi.org/10.3389/fncom.2011.00046
  9. Wang X. Neurophysiological and computational principles of cortical rhythms in cognition. Physiological Reviews, 2010, vol. 90, iss. 3, pp. 1195–1268. https://doi.org/10.1152/physrev.00035.2008
  10. Zeitler M., Daffertshofer A., Gielen C. Asymmetry in pulse-coupled oscillators with delay. Physical Review E, 2009, vol. 79, article no. 065203. https://doi.org/10.1103/PhysRevE.79.065203
  11. Pikovsky A., Rosenblum M., Kurths J. Synchronization: A universal concept in nonlinear science. Cambridge University Press, 2001. 432 p. https://doi.org/10.1017/CBO9780511755743
  12. Tsybina Y., Kastalskiy I., Kazantsev V., Gordleeva S. Synchronization events in a spiking neural network. 2022 Fourth International Conference Neurotechnologies And Neurointerfaces (CNN), 2022, pp. 206–208. https://doi.org/10.1109/CNN56452.2022.9912521
  13. Simonov A., Gordleeva S. Synchronization with an arbitrary phase shift in a pair of synaptically coupled neural oscillators. JETP Letters, 2014, vol. 98, iss. 10, pp. 632–637. https://doi.org/10.1134/S0021364013230136
  14. Barabash N., Levanova T., Stasenko S. Rhythmogenesis in the mean field model of the neuron-glial network. The European Physical Journal Special Topics, 2023, pp. 1–6. https://doi.org/10.1140/epjs/s11734-023-00778-9
  15. Stasenko S., Kazantsev V. 3D model of bursting activity generation. 2022 Fourth International Conference Neurotechnologies And Neurointerfaces (CNN), 2022, pp. 176–179. https://doi.org/10.1109/CNN56452.2022.9912507
  16. Olenin S., Levanova T., Stasenko S. Dynamics in the Reduced Mean-Field Model of Neuron-Glial Interaction. Mathematics, 2023, vol. 11, iss. 9, pp. 2143. https://doi.org/10.3390/math11092143
  17. Makovkin S., Kozinov E., Ivanchenko M., Gordleeva S. Controlling synchronization of gamma oscillations by astrocytic modulation in a model hippocampal neural network. Scientific Reports, 2022, vol. 12, article no. 6970. https://doi.org/10.1038/s41598-022-10649-3
  18. Stasenko S., Hramov A., Kazantsev V. Loss of neuron network coherence induced by virus-infected astrocytes: A model study. Scientific Reports, 2023, vol. 13, article no. 6401. https://doi.org/10.1038/s41598-023-33622-0
  19. Stasenko S., Kazantsev V. Dynamic Image Representation in a Spiking Neural Network Supplied by Astrocytes. Mathematics, 2023, vol. 11, iss. 3, article no. 561. https://doi.org/10.3390/math11030561
  20. Dityatev A., Rusakov D. Molecular signals of plasticity at the tetrapartite synapse. Current Opinion in Neurobiology, 2011, vol. 21, iss. 2, pp. 353–359. https://doi.org/10.1016/j.conb.2010.12.006
  21. Kazantsev V., Gordleeva S., Stasenko S., Dityatev A. A homeostatic model of neuronal firing governed by feedback signals from the extracellular matrix. PloS ONE, 2012, vol. 7, iss. 7, article no. e41646.
  22. Rich M., Wenner P. Sensing and expressing homeostatic synaptic plasticity. Trends in Neurosciences, 2007, vol. 30, iss. 3, pp. 119–125. https://doi.org/10.1371/journal.pone.0041646
  23. Turrigiano G. Homeostatic signaling: The positive side of negative feedback. Current Opinion in Neurobiology, 2007, vol. 17, iss. 3, pp. 318–324. https://doi.org/10.1016/j.conb.2007.04.004
  24. Kochlamazashvil G., Henneberger C., Bukalo O., Dvoretskova E., Senkov O., Lievens P., Westenbroek R., Engel A., Catterall W., Rusakov D., Schachner M., Dityatev A. The extracellular matrix molecule hyaluronic acid regulates hippocampal synaptic plasticity by modulating postsynaptic L-type Ca2+ channels. Neuron, 2010, vol. 67, iss. 1, pp. 116–128. https://doi.org/10.1016/j.neuron.2010.05.030
  25. Hodgkin A., Huxley A. A quantitative description of membrane current and its application to conduction and excitation in nerve. The Journal of Physiology, 1952, vol. 117, iss. 4, pp. 500–544. https://doi.org/10.1113%2Fjphysiol.1952.sp004764
  26. Fawcett J., Fyhn M., Jendelova P., Kwok J., Ruzicka J., Sorg B. The extracellular matrix and perineuronal nets in memory. Molecular Psychiatry, 2022, vol. 27, pp. 3192–3203. https://doi.org/10.1038/s41380-022-01634-3
  27. Dityatev A. Remodeling of extracellular matrix and epileptogenesis. Epilepsia, 2010, vol. 51, iss. 3, pp. 61–65. https://doi.org/10.1111/j.1528-1167.2010.02612.x
  28. Dityatev A., Fellin T. Extracellular matrix in plasticity and epileptogenesis. Neuron Glia Biology, 2008, vol. 4, iss. 3, pp. 235–247. https://doi.org/10.1017/s1740925x09000118
  29. Jong J., Broekaart D., Bongaarts A., Mühlebner A., Mills J., Vliet E., Aronica E. Altered Extracellular Matrix as an Alternative Risk Factor for Epileptogenicity in Brain Tumors. Biomedicines, 2022, vol. 10, iss. 10, article no. 2475. https://doi.org/10.3390/biomedicines10102475
  30. Lobov S., Zharinov A., Makarov V., Kazantsev V. Spatial memory in a spiking neural network with robot embodiment. Sensors, 2021, vol. 21, iss. 8, article no. 2678. https://doi.org/10.3390/s21082678
  31. Kim J., Lee H., Cho W., Lee K. Encoding information into autonomously bursting neural network with pairs of time-delayed pulses. Scientific Reports, 2019, vol. 9, article no. 1394. https://doi.org/10.1038/s41598-018-37915-7
  32. Lundqvis M., Rose J., Herman P., Brincat S., Buschman T. Miller E. Gamma and beta bursts underlie working memory. Neuron, 2016, vol. 90, iss. 1, pp. 152–164. https://doi.org/10.1016/j.neuron.2016.02.028
  33. Sokolov I., Azieva A., Burtsev M. Patterns of spiking activity of neuronal networks in vitro as memory traces. Biologically Inspired Cognitive Architectures (BICA) for Young Scientists: Proceedings of The First International Early Research Career Enhancement School (FIERCES 2016), 2016, pp. 241–247. https://doi.org/10.1007/978-3-319-32554-5_31
  34. Lam D., Enright H., Cadena J., Peters S., Sales A., Osburn J., Soscia D., Kulp K., Wheeler E., Fischer N. Tissue-specific extracellular matrix accelerates the formation of neural networks and communities in a neuronglia co-culture on a multi-electrode array. Scientific Reports, 2019, vol. 9, article no. 4159. https://doi.org/10.1038/s41598-019-40128-1
  35. Bikbaev A., Frischknecht R., Heine M. Brain extracellular matrix retains connectivity in neuronal networks. Scientific Reports, 2015, vol. 5, article no. 14527. https://doi.org/10.1038/srep14527
  36. Izhikevich E. Dynamical Systems in Neuroscience: The Geometry of Excitability and Bursting. Dynamical Systems. The MIT Press, 2007, First. 458 p. https://doi.org/10.7551/mitpress/2526.001.0001
  37. Lazarevich I., Stasenko S., Rozhnova M., Pankratova E., Dityatev A., Kazantsev V. Activity-dependent switches between dynamic regimes of extracellular matrix expression. PLoS ONE, 2020, vol. 15, iss. 1, article no. e0227917. https://doi.org/10.1371/journal.pone.0227917
  38. Rozhnova M., Pankratova E., Stasenko S., Kazantsev V. Bifurcation analysis of multistability and oscillation emergence in a model of brain extracellular matrix. Chaos, Solitons & Fractals, 2021, vol. 151, article no. 111253. https://doi.org/10.1016/j.chaos.2021.111253
  39. Sterrat D., Graham B., Gillies A., Willshaw D. Principles of computational modelling in neuroscience. Cambridge University Press, 2011. 404 p. https://doi.org/10.1017/CBO9780511975899
  40. Frischknecht R., Gundelfinger E. The brain’s extracellular matrix and its role in synaptic plasticity. Synaptic Plasticity, 2012, vol. 970, pp. 153–171. https://doi.org/10.1007/978-3-7091-0932-8_7
  41. Van Rossum Guido, Drake Fred L. Python 3 Reference Manual. CreateSpace Independent Publishing Platform, 2015. 364 p.
  42. Nelli F. Python data analytics: Data analysis and science using pandas, matplotlib, and the python programming language. Apress, 2015. 364 pp.
  43. Stimberg M., Brette R., Goodman D. Brian 2, an intuitive and efficient neural simulator. Elife, 2019, vol. 8, article no. e47314. https://doi.org/10.7554/eLife.47314
  44. Bisong E. Building machine learning and deep learning models on google cloud platform. Apress Berkeley, CA, 2019. 740 p. https://doi.org/10.1007/978-1-4842-4470-8
  45. Virtanen P., Gommers R., Oliphant T., Haberland M., Reddy T., Cournapeau D., Burovski E., Peterson P., Weckesser W., Bright J. SciPy 1.0: Fundamental algorithms for scientific computing in Python. Nature Methods, 2020, vol. 17, pp. 261–272. https://doi.org/10.1038/s41592-019-0686-2
  46. Duarte M. detecta: A Python module to detect events in data. GitHub Repository, 2020. Available at: https://github.com/demotu/detecta (accessed March 10, 2024).
  47. Gerstner W., Kistler W., Naud R., Paninski L. Neuronal dynamics: From single neurons to networks and models of cognition [Internet]. Cambridge University Press, 2014. https://doi.org/10.1017/CBO9781107447615
  48. Stasenko S., Kazantsev V. Mean-field model of tetrapartite synapse. 2022 Fourth International Conference Neurotechnologies And Neurointerfaces (CNN), 2022, pp. 180–184. https://doi.org/10.1109/CNN56452.2022.9912561
  49. Dityatev A. Remodeling of extracellular matrix and epileptogenesis. Epilepsia, 2010, vol. 51, iss. 3, pp. 61–65. https://doi.org/10.1111/j.1528-1167.2010.02612.x
  50. Dityatev A., Fellin T. Extracellular matrix in plasticity and epileptogenesis. Neuron Glia Biology, 2008, vol. 4, iss. 3, pp. 235–247. https://doi.org/10.1017/s1740925x09000118
  51. Bonneh-Barkay D., Wiley C. Brain extracellular matrix in neurodegeneration. Brain Pathology, 2009, vol. 19, iss. 4, pp. 573–585. https://doi.org/10.1111/j.1750-3639.2008.00195.x
  52. Khoshneviszadeh M., Jandke S., Kaushik R., Ulbrich P., Norman O., Jukkola J., Heikkinen A., Schreiber S., Dityatev A. Microvascular damage, neuroinflammation and extracellular matrix remodeling in Col18a1 knockout mice as a model for early cerebral small vessel disease. Matrix Biol., 2024, vol. 128, pp. 39–64. https://doi.org/10.1016/j.matbio.2024.02.007
  53. Ulbrich P., Khoshneviszadeh M., Jandke S., Schreiber S., Dityatev A. Interplay between perivascular and perineuronal extracellular matrix remodelling in neurological and psychiatric diseases. European Journal of Neuroscience, 2021, vol. 53, iss. 12, pp. 3811–3830. https://doi.org/10.1111/ejn.14887
  54. Broekaart D. W., Bertran A., Jia S., Korotkov A., Senkov O., Bongaarts A., Mills J. D., Anink J. J., Seco J., Baayen J. C., Idema S., Chabrol E., Becker A. J., Wadman W. J., Tarragó T., Gorter J. A., Aronica E., Prades R., Dityatev A., Vliet E. A. van The matrix metalloproteinase inhibitor IPR-179 has antiseizure and antiepileptogenic effects. The Journal of Clinical Investigation, 2021, vol. 131, iss. 1, article no. e138332. https://doi.org/10.1172/jci138332
Received: 
15.03.2024
Accepted: 
02.04.2024
Published: 
28.06.2024
  • 290 reads