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

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Sysoeva M. V., Sysoev I. V., Ponomarenko V. I., Prokhorov M. D. Reconstructing the neuron-like oscillator equations modeled by a phase-locked system with delay from scalar time series. Izvestiya VUZ. Applied Nonlinear Dynamics, 2020, vol. 28, iss. 4, pp. 397-413. DOI: 10.18500/0869-6632-2020-28-4-397-413

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Reconstructing the neuron-like oscillator equations modeled by a phase-locked system with delay from scalar time series

Sysoeva Marina Vyacheslavovna, Yuri Gagarin State Technical University of Saratov
Sysoev Ilya V., Saratov State University
Ponomarenko Vladimir Ivanovich, Saratov Branch of Kotel`nikov Institute of Radiophysics and Electronics of Russian Academy of Sciences
Prokhorov Mihail Dmitrievich, Saratov Branch of Kotel`nikov Institute of Radiophysics and Electronics of Russian Academy of Sciences

The purpose of this work is to develop the reconstruction technique for the neuron-like oscillator equations descibed by a phase-locked system model with delay from scalar time series. Methods. We reconstruct the state vector given a scalar series of only one variable corresponding to the transmembrane potential. The second variable is obtained by numerical differentiation with smoothing by a polynomial. The third variable is obtained by numerical integration using the Simpson method. Next, the target function describing the length of the nonlinear function at a trial delay time is constructed and minimized. Results. The delay time, effective system parameters, and nonlinear function can be reconstructed using the proposed method. The method gives correct results in various periodic and chaotic regimes, including intermittency. The method works even in presence of 1% measurement noise. Conclusion. The described method is useful as a tools for reconstructing neuron models from experimental data of extracellular or intracellular recordings from the brain or in culture.


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