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


For citation:

Pavlov A. N., Dumskij D. V. Return time dynamics in dependence to choice of the poincare section. Izvestiya VUZ. Applied Nonlinear Dynamics, 2003, vol. 11, iss. 6, pp. 65-74. DOI: 10.18500/0869-6632-2003-11-6-65-74

This is an open access article distributed under the terms of Creative Commons Attribution 4.0 International License (CC-BY 4.0).
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Russian
Article type: 
Article
UDC: 
517.9

Return time dynamics in dependence to choice of the poincare section

Autors: 
Pavlov Aleksej Nikolaevich, Saratov State University
Dumskij Dmitrij Viktorovich, Saratov State University
Abstract: 

In this paper we study how different measures of chaotic dynamics estimated from return time sequences are sensitive to choice оf the Poincare section. We show that dynamical characteristics оf chaotic oscillations аrе less sensitive to displacements оf the secant plane than metrical and scaling characteristics of evolution dynamics. 

Key words: 
Acknowledgments: 
The authors are grateful to Prof. W. Ebeling for numerous discussions and discussion of the results. The research was supported by the Ministry of Education of the Russian Federation and CRDF (grants Y1-P-06-06, SR-006-X1), and partially supported by RFBR grant 04-02-16769 and Presidential grant (MK-2512.2004.2).
Reference: 
  1. Sauer Т. Reconstruction оf dynamical system from interspike intervals. Phys. Rev. Lett. 1994;72(24):3811-3814. DOI: 10.1103/physrevlett.72.3811.
  2. Longtin А, Bulsara А, Moss Е. Time interval sequences in the bistable systems and the noise induced transmission of information by sensory neurons. Phys.Rev.Lett. 1991;67(5):656-659. DOI: 10.1103/physrevlett.67.656; Douglass JK, Wilkens L, Pantazelou E, Moss Е. Noise enchancement оf the information in crayfish mechanoreceptors by stochastic resonance. Nature. 1993;365(6444):337-340. DOI: 10.1038/365337a0; Moss F, Pei Х. Neurons in parallel. Nature. 1995;376:211-212. DOI: 10.1038/376211a0; Richardson KA, Imhoff TT, Grigg Р, Collins JJ. Encoding chaos in neural spike train. Phys. Rev. Lett. 1998;80(11):2485-2488. DOI: 10.1103/PhysRevLett.80.2485.
  3. Castro R, Sauer Т. Correlation dimension оf attractors through interspike intervals. Phys. Rev. E. 1997;55(1):287-290. DOI: 10.1103/PhysRevE.55.287.
  4. Hegger R, Kantz H. Embedding of sequence оf time intervals. Europhysics Letters. 1997;38(4):267. DOI: 10.1209/epl/i1997-00236-0.
  5. Racicot DM, Longtin A. Interspike interval attractors from chaotically driven neuron models. Physica D. 1997;104(2):184-204. DOI: 10.1016/S0167-2789(97)00296-0.
  6. Sauer T. Reconstruction of integrate-and-fire dynamics. In: Nonlinear Dynamics and Time Series, Culter C, Kaplan D, editors. Fields Institute Communications. Vol. 11. Providence, RI: American Mathematical Society; 1997. P. 63. DOI: 10.1090/fic/011/05.
  7. Pavlov AN, Sosnovtseva ОV, Mosekilde E, Anishchenko VS. Extracting dynamics from threshold-crossing interspike intervals: possibilities and limitations. Phys. Rev. E. 2000;61(5):5033-5044. DOI: 10.1103/PhysRevE.61.5033.
  8. Pavlov АN, Sosnovtseva ОV, Mosekilde E, Anishchenko VS. Chaotic dynamics from interspike intervals. Phys. Rev. Е. 2001;63(3):036205. DOI: 10.1103/PhysRevE.63.036205.
  9. Grassberger Р, Procaccia I. Measuring the strangeness оf strange attractors. Physica D. 1983;9(1-2):189-208. DOI: 10.1016/0167-2789(83)90298-1.
  10. Janson NB, Раvlоv AN, Neiman AB, Anishchenko VS. Reconstruction оf dynamical and geometrical properties оf chaotic attractors from threshold-crossing interspike intervals. Phys. Rev. Е. 1998;58(1):R4-R7. DOI: 10.1103/PhysRevE.58.R4.
  11. Wolf A, Swift JB, Swinney HL, Vastano JA. Determining Lyapunov exponents from а time series. Physica D. 1985;16(3):285-317. DOI: 10.1016/0167-2789(85)90011-9.
  12. Farmer JD, Sidorowich JJ. Predicting chaotic time series. Phys. Rev. Lett. 1987;59(8):845-848. DOI: 10.1103/PhysRevLett.59.845.
  13. Muzy JF, Bacry E, Arneodo A. Wavelets and multifractal formalism for singular signals: application to turbulence data. Phys. Rev. Lett. 1991;67(25):3515-3518. DOI: 10.1103/PhysRevLett.67.3515; Muzy JF, Bacry E, Arneodo А. The multifractal formalism revisited with wavelets. Int. J. Bifurc. and Chaos. 1994;4(2):245-302. DOI: 10.1142/S0218127494000204; Arneodo А, Decoster N, Roux SG. Intermittency, log-normal statistics, and multifractal cascade process in high-resolution satellite images of cloud structure. Phys. Rev. Lett. 1999;83(6):1255-1258. DOI: 10.1103/PhysRevLett.83.1255.
  14. Peng C-K, Havlin S, Stanley HE, Goldberger AL. Quantification of scaling exponents and crossover phenomena in nonstationary heartbeat time series. Chaos. 1995;5(1):82-87. DOI: 10.1063/1.166141.
  15. Ebeling W, Nicolis G. Entropy of symbolic sequences: the role of correlations. Europhys. Lett. 1991;14(3):191. DOI: 10.1209/0295-5075/14/3/001; Ebeling W, Nicolis С. Word frequency and entropy of symbolic sequences: а dynamical perspective. Chaos, Solitons and Fractals. 1992;2(6):635-650. DOI: 10.1016/0960-0779(92)90058-U.
  16. Kaspar F, Schuster HG. Easily calculable measure for the complexity of spatiotemporal patterns. Phys. Rev. А. 1987;36(2):842-848. DOI: 10.1103/PhysRevA.36.842.
Received: 
03.02.2003
Accepted: 
10.04.2003
Available online: 
06.12.2023
Published: 
31.12.2003