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Russian
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Article
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517.926
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A new approach to mathematical modeling of chemical synapses

Autors: 
Glyzin Dmitriy Sergeyevich, National Research University "Higher School of Economics"
Glyzin Sergey Dmitrievich, P. G. Demidov Yaroslavl State University
Kolesov A. Yu., P. G. Demidov Yaroslavl State University
Abstract: 

The purpose of this work is to study a new mathematical model of a ring neural network with unidirectional chemical connections, which is a singularly perturbed system of differential-difference equations with delay.

Methods. A combination of analytical and numerical methods is used to study the existence and stability of special periodic solutions in this system, the so-called traveling waves.

Results. The proposed methods make it possible to show that the ring system under study allows the number of stable traveling waves to increase with the number of oscillators in the network.

Conclusion. In this article, we rethink and refine the previously proposed method of mathematical modeling of chemical synapses. On the one hand, it was possible to fully take into account the requirement of the Volterra structure of the corresponding equations and, on the other hand, the hypothesis of saturating conductivity. This makes it possible to observe the principle of uniformity: the new mathematical model is based on the same principles as the previously proposed model of electrical synapses.

Acknowledgments: 
This work was supported by the Russian Science Foundation (project No. 22-11-00209), https://rscf.ru/project/22-11-00209/
Reference: 
  1. Hodgkin AL, Huxley AF. A quantitative description of membrane current and its application to conduction and excitation in nerve. Journal of Physiology. 1952;117:500–544. DOI: 10.1113/ jphysiol.1952.sp004764.
  2. Izhikevich Eugene M. Dynamical Systems in Neuroscience: The Geometry of Excitability and Bursting. Cambridge, Massachusetts, USA: The MIT Press; 2007. 464 p.
  3. Kolesov AY, Kolesov YS. Relaxational oscillations in mathematical models of ecology. Proc. Steklov Inst. Math. 1995;199:1–126.
  4. Maiorov VV, Myshkin IY. Mathematical modeling of a neuron net on the basis of the equation with delays. Math. Models Comput. Simul. 1990;2(11):64–76 (in Russian).
  5. Hutchinson GE. Circular causal systems in ecology // Ann. N. Y. Acad. of Sci. 1948;50(4): 221–246. DOI: 10.1111/j.1749-6632.1948.tb39854.x.
  6. Glyzin SD, Kolesov AYu, Rozov NKh. Self-excited relaxation oscillations in networks of impulse neurons. Russian Math. Surveys. 2015;70(3):383–452. DOI: 10.1070/RM2015v070n03ABEH 004951.
  7. Kolesov AYu, Mishchenko EF, Rozov NKh. A modification of Hutchinson’s equation. Comput. Math. Math. Phys. 2010;50(12):1990–2002. DOI: 10.1134/S0965542510120031.
  8. Glyzin SD, Kolesov AYu, Rozov NKh. On a method for mathematical modeling of chemical synapses. Differential Equations. 2015;70(3):383–452. DOI: 10.1134/S0012266113100017.
  9. Somers D, Kopell N. Rapid synchronization through fast threshold modulation. Biol. Cybern. 1993;68:393–407. DOI: 10.1007/BF00198772.
  10. Kopell N, Somers D. Anti-phase solutions in relaxation oscillators coupled through excitatory interactions. J. Math. Biol. 1995;33:261–280. DOI: 10.1007/BF00169564.
  11. Mishchenko EF, Rozov NKh. Differential Equations with Small Parameters and Relaxation Oscillations. New York, NY: Springer; 1980. 228 p. DOI: 10.1007/978-1-4615-9047-7.
  12. FitzHugh RA. Impulses and physiological states in theoretical models of nerve membrane. Biophysical J. 1961;1(6):445–466. DOI: 10.1016/S0006-3495(61)86902-6.
  13. Terman D. An Introduction to Dynamical Dystems and Neuronal Dynamics. In: Tutorials in Mathematical Biosciences I. Lecture Notes in Mathematics. Berlin, Heidelberg: Springer-Verlag; 2005. P. 21–68.DOI: 10.1007/978-3-540-31544-5_2.
  14. Glyzin SD, Kolesov AYu. On a Method of Mathematical Modeling of Electrical Synapses. Diff Equat. 2022;58:853–868. DOI: 10.1134/S0012266122070011.
  15. Kolesov AYu, Mishchenko EF, Rozov NKh. Relay with delay and its C1-approximation. Proc. Steklov Inst. Math. 1997;216:119–146.
  16. Glyzin SD, Kolesov AYu. Traveling waves in fully coupled networks of linear oscillators. Comput. Math. Math. Phys. 2022;62(1):66–83. DOI: 10.1134/S0965542522010079.
  17. Glyzin SD, Kolesov AYu, Rozov NKh. Buffering in cyclic gene networks. Theoret. and Math. Phys. 2016;187(3)935–951. DOI: 10.1134/S0040577916060106.
  18. Brown PN, Byrne GD, Hindmarsh AC. VODE: A Variable Coefficient ODE Solver // SIAM J. Sci. Stat. Comput. 1989;10(5):1038–1051.DOI: 10.1137/0910062.
  19. Glyzin SD, Kolesov AYu. Periodic two-cluster synchronization modes in fully coupled networks of nonlinear oscillators. Theor. Math. Phys. 2022;212:1073–1091. DOI: 10.1134/S0040577922080049.
Received: 
13.05.2023
Accepted: 
15.01.2024
Available online: 
22.03.2024