ISSN 0869-6632 (Print)
ISSN 2542-1905 (Online)

For citation:

Bazhanova M. V., Gordleeva S. Y., Kazantsev V. B., Lobov S. А. Control of network bursting discharges by local electrical stimulation in spiking neuron network. Izvestiya VUZ. Applied Nonlinear Dynamics, 2021, vol. 29, iss. 3, pp. 428-439. DOI: 10.18500/0869-6632-2021-29-3-428-439

This is an open access article distributed under the terms of Creative Commons Attribution 4.0 International License (CC-BY 4.0).
Full text PDF(Ru):
PDF icon gordleeva.pdf
(downloads: 888)
Language: 
English
Heading: 
Nonlinear Dynamics and Neuroscience
Article type: 
Article
UDC: 
530.182
DOI: 
10.18500/0869-6632-2021-29-3-428-439

Control of network bursting discharges by local electrical stimulation in spiking neuron network

Autors: 
Bazhanova Milena Viktorovna, Lobachevsky State University of Nizhny Novgorod
Gordleeva Susanna Yurevna, Lobachevsky State University of Nizhny Novgorod
Kazantsev Viktor Borisovich, Institute of Applied Physics of the Russian Academy of Sciences
Lobov Sergey Анатольевич, Lobachevsky State University of Nizhny Novgorod
Abstract: 

Goal. The paper is devoted to controlling the dynamics of spike neural networks by local periodic stimulation of various network sections. Methods. The simulation uses a network of synaptically connected spike neurons distributed in two-dimensional space. The dynamics of the transmembrane potential of neurons is described by the Izhikevich model, short-term synaptic plasticity is represented by the model Tsodyksa–Markram, the effects of changes in the efficiency of connections between neurons are modeled using spike-timing-dependent plasticity (STDP). Results. It is shown that the model reproduces the dynamics of living neural networks grown under in vitro conditions quite well. In its spontaneous dynamics, such a network exhibits a wide range of dynamic modes, including asynchronous spikes and quasi-synchronous spike bundles. It was found that due to STDP, the network can adapt to the stimulating signal, so that the network bundles become synchronized (phase-synchronized) with the stimulation signal. The analysis of the dependence of this effect on the parameters of stimulation, in particular, on the geometric dimensions of the stimulated area, as well as the connectivity of the network. Conclusion. With the help of local periodic stimulation of a part of the neural network,when selecting certain parameters of the stimulating signal, taking into account the characteristics of the network, externalcontrol of the dynamics of the spike neural network is possible.

Key words: 
mathematical modeling
spiking neuron network
control
stimulation
Acknowledgments: 
The work was supported by Center for Technologies in Robotics and Mechatronics Components, Innopolis University
Reference: 
  1. Klimesch W. Memory processes, brain oscillations and EEG synchronization. International Journal of Psychophysiology. 1996;24(1–2):61–100. DOI: 10.1016/s0167-8760(96)00057-8.
  2. Llinas R. I of the Vortex: From Neurons to Self. The MIT Press Cambridge, Massachusetts; 2002. 320 p.
  3. Mohns EJ, Blumberg MS. Synchronous bursts of neuronal activity in the developing hippocampus: Modulation by active sleep and association with emerging gamma and theta rhythms. Journal of Neuroscience. 2008;28(40):10134–10144. DOI: 10.1523/JNEUROSCI.1967-08.2008.
  4. Jutras MJ, Buffalo EA. Synchronous neural activity and memory formation. Current Opinion in Neurobiology. 2010;20(2):150–155. DOI: 10.1016/j.conb.2010.02.006.
  5. Benedek M, Bergner S, Konen T, Fink A, Neubauer AC. EEG alpha synchronization is related to top-down processing in convergent and divergent thinking. Neuropsychologia. 2011;49(12):3505– 3511. DOI: 10.1016/j.neuropsychologia.2011.09.004.
  6. Cantero JL, Atienza M. The role of neural synchronization in the emergence of cognition across the wake-sleep cycle. Reviews in the Neurosciences. 2005;16(1):69–83. DOI: 10.1515/revneuro.2005.16.1.69.
  7. Hermundstad AM, Brown KS, Bassett DS, Aminoff EM, Frithsen A, Johnson A, Tipper CM, Miller MB, Grafton ST, Carlson JM. Structurally-constrained relationships between cognitive states in the human brain. PLoS Computational Biology. 2014;10(5):e1003591. DOI: 10.1371/journal.pcbi.1003591.
  8. Botvinick M, Braver T. Motivation and cognitive control: From behavior to neural mechanism. Annual Review of Psychology. 2015;66:83–113. DOI: 10.1146/annurev-psych-010814-015044.
  9. Wang XJ. Neural dynamics and circuit mechanisms of decision-making. Current Opinion in Neurobiology. 2012;22(6):1039–1046. DOI: 10.1016/j.conb.2012.08.006.
  10. Funahashi S. Prefrontal contribution to decision-making under free-choice conditions. Frontiers in Neuroscience. 2017;11:431. DOI: 10.3389/fnins.2017.00431.
  11. Spencer KM, Nestor PG, Niznikiewicz MA, Salisbury DF, Shenton ME, McCarley RW. Abnormal neural synchrony in schizophrenia. Journal of Neuroscience. 2003;23(19):7407–7411. DOI: 10.1523/JNEUROSCI.23-19-07407.2003.
  12. Uhlhaas PJ, Singer W. High-frequency oscillations and the neurobiology of schizophrenia. Dialogues in Clinical Neuroscience. 2013;15(3):301–313. DOI: 10.31887/DCNS.2013.15.3/puhlhaas.
  13. Levy R, Hutchison WD, Lozano AM, Dostrovsky JO. High-frequency synchronization of neuronal activity in the subthalamic nucleus of Parkinsonian patients with limb tremor. Journal of Neuroscience. 2000;20(20):7766–7775. DOI: 10.1523/JNEUROSCI.20-20-07766.2000.
  14. Rubchinsky LL, Park C, Worth RM. Intermittent neural synchronization in Parkinson’s disease. Nonlinear Dynamics. 2012;68(3):329–346. DOI: 10.1007/s11071-011-0223-z.
  15. Nambu A, Tachibana Y, Chiken S. Cause of parkinsonian symptoms: Firing rate, firing pattern or dynamic activity changes? Basal Ganglia. 2015;5(1):1–6. DOI: 10.1016/j.baga.2014.11.001.
  16. Scharfman HE. The neurobiology of epilepsy. Current Neurology and Neuroscience Reports. 2007;7(4):348–354. DOI: 10.1007/s11910-007-0053-z.
  17. Jiruska P, de Curtis M, Jefferys JGR, Schevon CA, Schiff SJ, Schindler K. Synchronization and desynchronization in epilepsy: Controversies and hypotheses. The Journal of Physiology. 2013;591(4):787–797. DOI: 10.1113/jphysiol.2012.239590.
  18. Gordleeva S, Kanakov O, Ivanchenko M, Zaikin A, Franceschi C. Brain aging and garbage cleaning. Seminars in Immunopathology. 2020;42(5):647–665. DOI: 10.1007/s00281-020-00816-x.
  19. Whitwell HJ, Bacalini MG, Blyuss O, Chen S, Garagnani P, Gordleeva SY, Jalan S, Ivanchenko M, Kanakov O, Kustikova V, Marino IP, Meyerov I, Ullner E, Franceschi C, Zaikin A. The human body as a super network: Digital methods to analyze the propagation of aging. Frontiers in Aging Neuroscience. 2020;12:136. DOI: 10.3389/fnagi.2020.00136.
  20. Shahaf G, Marom S. Learning in networks of cortical neurons. Journal of Neuroscience. 2001; 21(22):8782–8788. DOI: 10.1523/JNEUROSCI.21-22-08782.2001.
  21. Baruchi I, Ben-Jacob E. Towards neuro-memory-chip: Imprinting multiple memories in cultured neural networks. Phys. Rev. E. 2007;75(5):050901. DOI: 10.1103/PhysRevE.75.050901.
  22. Bakkum DJ, Chao ZC, Potter SM. Spatio-temporal electrical stimuli shape behavior of an embodied cortical network in a goal-directed learning task. Journal of Neural Engineering. 2008;5(3):310–323. DOI: 10.1088/1741-2560/5/3/004.
  23. Pimashkin A, Gladkov A, Mukhina I, Kazantsev V. Adaptive enhancement of learning protocol in hippocampal cultured networks grown on multielectrode arrays. Frontiers in Neural Circuits. 2013;7:87. DOI: 10.3389/fncir.2013.00087.
  24. Maeda E, Robinson HP, Kawana A. The mechanisms of generation and propagation of synchronized bursting in developing networks of cortical neurons. Journal of Neuroscience. 1995;15(10): 6834–6845. DOI: 10.1523/JNEUROSCI.15-10-06834.1995.
  25. Wagenaar DA, Pine J, Potter SM. An extremely rich repertoire of bursting patterns during the development of cortical cultures. BMC Neuroscience. 2006;7:11. DOI: 10.1186/1471-2202-7-11.
  26. 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;5:46. DOI: 10.3389/fncom.2011.00046.
  27. Lobov S, Simonov A, Kastalskiy I, Kazantsev V. Network response synchronization enhanced by synaptic plasticity. The European Physical Journal Special Topics. 2016;225(1):29–39. DOI: 10.1140/epjst/e2016-02614-y.
  28. Lobov SA, Zhuravlev MO, Makarov VA, Kazantsev VB. Noise enhanced signaling in STDP driven spiking-neuron network. Mathematical Modelling of Natural Phenomena. 2017;12(4):109–124. DOI: 10.1051/mmnp/201712409.
  29. Chao ZC, Bakkum DJ, Potter SM. Region-specific network plasticity in simulated and living cortical networks: Comparison of the center of activity trajectory (CAT) with other statistics. Journal of Neural Engineering. 2007;4(3):294–308. DOI: 10.1088/1741-2560/4/3/015.
  30. Izhikevich EM. Simple model of spiking neurons. IEEE Transactions on Neural Networks. 2003;14(6):1569–1572. DOI: 10.1109/TNN.2003.820440.
  31. Izhikevich EM. Which model to use for cortical spiking neurons? IEEE Transactions on Neural Networks. 2004;15(5):1063–1070. DOI: 10.1109/TNN.2004.832719.
  32. Tsodyks M, Pawelzik K, Markram H. Neural networks with dynamic synapses. Neural Computation. 1998;10(4):821–835. DOI: 10.1162/089976698300017502.
  33. Morrison A, Diesmann M, Gerstner W. Phenomenological models of synaptic plasticity based on spike timing. Biological Cybernetics. 2008;98(6):459–478. DOI: 10.1007/s00422-008-0233-1.
  34. Song S, Miller KD, Abbott LF. Competitive Hebbian learning through spike-timing-dependent synaptic plasticity. Nature Neuroscience. 2000;3(9):919–926. DOI: 10.1038/78829.
  35. Braitenberg V, Schuz A. Anatomy of the Cortex: Statistics and Geometry. Vol. 18 of Studies of Brain Function. Springer-Verlag Berlin Heidelberg; 1991. 251 p. DOI: 10.1007/978-3-662-02728-8.
  36. Esir PM, Gordleeva SY, Simonov AY, Pisarchik AN, Kazantsev VB. Conduction delays can enhance formation of up and down states in spiking neuronal networks. Phys. Rev. E. 2018;98(5): 052401. DOI: 10.1103/PhysRevE.98.052401.
  37. Kazantsev VB, Asatryan SY. Bistability induces episodic spike communication by inhibitory neurons in neuronal networks. Phys. Rev. E. 2011;84(3):031913. DOI: 10.1103/PhysRevE.84.031913.
  38. Makovkin SY, Shkerin IV, Gordleeva SY, Ivanchenko MV. Astrocyte-induced intermittent synchronization of neurons in a minimal network. Chaos, Solitons & Fractals. 2020;138:109951. DOI: 10.1016/j.chaos.2020.109951.
  39. Pankratova EV, Kalyakulina AI, Stasenko SV, Gordleeva SY, Lazarevich IA, Kazantsev VB. Neuronal synchronization enhanced by neuron–astrocyte interaction. Nonlinear Dynamics. 2019; 97(1):647–662. DOI: 10.1007/s11071-019-05004-7.
  40. Simonov AY, Gordleeva SY, Pisarchik AN, Kazantsev VB. Synchronization with an arbitrary phase shift in a pair of synaptically coupled neural oscillators. JETP Letters. 2014;98(10):632–637. DOI: 10.1134/S0021364013230136.
  41. Andreev AV, Frolov NS, Pisarchik AN, Hramov AE. Chimera state in complex networks of bistable Hodgkin–Huxley neurons. Phys. Rev. E. 2019;100(2):022224. DOI: 10.1103/PhysRevE.100.022224.
  42. Andreev AV, Ivanchenko MV, Pisarchik AN, Hramov AE. Stimulus classification using chimeralike states in a spiking neural network. Chaos, Solitons & Fractals. 2020;139:110061. DOI: 10.1016/j.chaos.2020.110061.
Received: 
14.11.2020
Accepted: 
28.01.2021
Published: 
31.05.2021
  • 2360 reads