A new approach to the determination of the stellate neuron activity function in rat’s brain

  • O. Klymenko
  • A. Oleinick
  • C. Amatore
  • I. SvirEmail author
The International Conference “Modern Physical Chemistry for Advanced Materials”


In this work, we present the results of a mathematical modelling of NO· release by neurons and its transport in the brain by diffusion. The model is applied to analyze the experimental data on NO· release from a neuron monitored during its patch-clamp stimulation by an ultramicroelectrode introduced into a slice of living rat’s brain. The neuron activity function was obtained by numerical deconvolution of the experimental data using the response function of the electrode to an instantaneous spike of neuronal activity. The Gaussian decomposition of NO· release activity function allows qualitative and quantitative conclusions to be drawn about neuron activity. Since the integral activity function is readily obtained by deconvolution, the decomposition can be performed using other more relevant descriptions of NO· bursts emerging from active neurons.


Nitric Oxide Deconvolution Activity Function Nitric Oxide Release Nitric Oxide Concentration 
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  1. 1.
    A. Oleinick, C. Amatore, M. Guille, et al., Math. Med. Biol. 23, 27 (2006).CrossRefGoogle Scholar
  2. 2.
    A. Oleinick, C. Amatore, and I. Svir, Radioelektron. Inform. 3, 18 (2005).Google Scholar
  3. 3.
    A. Oleinick, C. Amatore, O. Klymenko, and I. Svir, Nonlinear Analysis: Modelling and Control 12(3), 399 (2007).Google Scholar
  4. 4.
    J. Sullivan. Nat. Neurosci. 6(9), 905 (2003).CrossRefGoogle Scholar
  5. 5.
    K.D. Micheva, J. Buchanan, R. W. Holz, and S. J. Smith, Nat. Neurosci. 6(9), 925 (2003).CrossRefGoogle Scholar
  6. 6.
    R. M. Wightman, C. Amatore, R. C. Engstrom, et al., Neuroscience 25(2), 513 (1988).CrossRefGoogle Scholar
  7. 7.
    C. Amatore, S. Arbault, Y. Chen, et al., Lab. Chip 7, 233 (2007).CrossRefGoogle Scholar
  8. 8.
    C. Amatore, S. Arbault, Y. Bouret, et al., Chem. Phys. Phys. Chem. 7, 181 (2006).Google Scholar
  9. 9.
    A. Rancillac, M. Guille, X.-K. Tong, et al., J. Neuroscience 26, 6997 (2006).CrossRefGoogle Scholar
  10. 10.
    V. L. Pogrebnaya, A. P. Usov, A. V. Nesterenko, and P. I. Bez’yazychnyi, Zh. Prikl. Khim. 48, 954 (1975).Google Scholar
  11. 11.
    A. Denicola, J. M. Souza, R. Radi, and E. Lissi. Arch. Biochem. Biophys. 328(1), 208 (1996).CrossRefGoogle Scholar
  12. 12.
    C. Amatore, A. Oleinick, and I. Svir. J. Electroanal. Chem. 553, 49 (2003).CrossRefGoogle Scholar
  13. 13.
    A. N. Tikhonov and A. A. Samarskii, Equations of Mathematical Physics (Nauka, Moscow, 1966) [in Russian].Google Scholar
  14. 14.
    M. Abramowitz and I. A. Stegun. Handbook of Mathematical Functions (National Bureau of Standards, Washington, D.C., 1972).Google Scholar
  15. 15.
    W. H. Press, S. A. Teukolsky, W. T. Vetterling, and B. P. Flannery, Numerical Recipes in C: The Art of Scientific Computing, 2nd ed. (Cambridge University Press, Cambridge, U.K., 1992).Google Scholar

Copyright information

© Pleiades Publishing, Ltd. 2008

Authors and Affiliations

  • O. Klymenko
    • 1
  • A. Oleinick
    • 1
    • 2
  • C. Amatore
    • 2
  • I. Svir
    • 1
    • 2
    Email author
  1. 1.Kharkov National University of RadioelectronicsKharkovUkraine
  2. 2.Ecole Normale Supérieure, Departement de ChimieUMR CNRS 8640 “PASTEUR,”ParisFrance

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