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


For citation:

Podladchikova L. N., Tikidji-Hamburyan R. A., Tikidji-Hamburyan A. V., Shevtsova N. A., Vasilkov V. A., Belova E. I., Ishenko I. A. Activity synchronization of different neuron types in the columns of the cerebral visual cortex. Izvestiya VUZ. Applied Nonlinear Dynamics, 2011, vol. 19, iss. 6, pp. 83-95. DOI: 10.18500/0869-6632-2011-19-6-83-95

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
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Article
UDC: 
612.825; 51–76

Activity synchronization of different neuron types in the columns of the cerebral visual cortex

Autors: 
Podladchikova Ljubov Nikolaevna, Research Institute of Neurocybernetics them. A.B. Kogan, Southern Federal University
Tikidji-Hamburyan Ruben Akimovich, Research Institute of Neurocybernetics them. A.B. Kogan, Southern Federal University
Tikidji-Hamburyan Aleksandra Vladlenovna, Research Institute of Neurocybernetics them. A.B. Kogan, Southern Federal University
Vasilkov Vjacheslav Aleksandrovich, Research Institute of Neurocybernetics them. A.B. Kogan, Southern Federal University
Belova Evgenija Ivanovna, Research Institute of Neurocybernetics them. A.B. Kogan, Southern Federal University
Ishenko Irina Aleksandrovna, Research Institute of Neurocybernetics them. A.B. Kogan, Southern Federal University
Abstract: 

The results of neurophysiological and modeling studies focused on activity synchronization among of different types of neurons and spike shape dynamics in two bistability transition states have been presented. In modeling study, spike duration range of «fast» и «slow» neurons recorded in neurophysiological experiments were simulated. While simulation of model element groups with different properties of short-term and long-term activity dynamics, it was revealed that degree of their activity synchronization depend on frequency and power of input influences; it was maximal at high frequency of super threshold input signals. Possible approach to the study of column functioning mechanisms and dynamics operations inside the columns have been considered.

Reference: 
  1. Kogan AB. Functional Organization of the Neural Mechanisms of the Brain. Leningrad: Meditsina; 1979. 224 p. (in Russian).
  2. Hubel D. Eye, Brain, and Vision. NY: W.H. Freeman; 1988. 227 p.
  3. Hubel DH, Wiesel TN. Shape and arrangement of columns in cat’s visual cortex. J. Physiol. 1963;165(3):559–568. DOI: 10.1113/jphysiol.1963.sp007079.
  4. Mountcastle VB. The columnar organization of the neocortex. Brain. 1997;120(4):701–722. DOI: 10.1093/brain/120.4.701.
  5. Freeman M. Cortical columns: a multi–parameter examination. Cerebral Cortex. 2003;13(1):70–72. DOI: 10.1093/cercor/13.1.70.
  6. Hirsch JA, Martinez LM. Laminar processing in the visual cortical column. Current Opinion in Neurobiology. 2006;16(4):377–384. DOI: 10.1016/j.conb.2006.06.014.
  7. Horton JC, Adams DL. The cortical column: a structure without a function. Phil. Trans. R. Soc. B. 2005;360(1456):837–862. DOI: 10.1098/rstb.2005.1623.
  8. Katzel D, Zemelman BV, Buetfering C, Wolfel M, Miesenbock G. The columnar and laminar organization of inhibitory connections to neocortical excitatory cells. Nature Neuroscience. 2011;14(1):100–107. DOI: 10.1038/nn.2687.
  9. Szentagothai J. The neuron network of the cerebral cortex: A functional interpretation. Proc. R. Soc. Lond. Series B. 1978;201(1144):219–248. DOI: 10.1098/rspb.1978.0043.
  10. Compte A, Sanchez-Vives MV, McCormick DA, Wang XJ. Cellular and network mechanisms of slow oscillatory activity (< 1 Hz) and wave propagations in a cortical network model. J. Neurophysiol. 2003;89(5):2707–2725. DOI: 10.1152/jn.00845.2002.
  11. Eckhorn R, Bauer R, Jordon W, Brosch M, Kruse W, Munk M, Reitboeck HJ. Coherent oscillations: a mechanisms of feature linking the visual cortex. Biol. Cybern. 1988;60(2):121–130. DOI: 10.1007/bf00202899.
  12. Gray SM, Singer W. Stimulus-specific neuronal oscillations in orientation columns of visual cortex. PNAS. 1989;86(5):1698–1702. DOI: 10.1073/pnas.86.5.1698.
  13. Hopfield JJ, Brody CD. What is moment? Transient synchrony as a collective mechanism for spatio-temporal integration. PNAS. 2001;98(3):1282–1287. DOI: 10.1073/pnas.98.3.1282.
  14. Podladchikova LN, Tikidzhi-Khamburyan RA, Bondar GG, Gusakova VI, Ivlev SA, Dunin-Barkovsky VL. Time dynamics of the activity of «fast» and «slow» neurons of the visual cortex of the brain and cerebellum. Neuro-Computers: Development and Application. 2004;(11):50–62 (in Russian).
  15. Podladchikova LN, Koltunova TI, Belova EI, Tikidzhi-Khamburyan RA, Ishchenko IA, Shaposhnikov DG. Neuroinformatics approach to the study of neural and systemic mechanisms of visual perception. In: Neuroinformatics - 2011. Lectures on Neuroinformatics: XIII All-Russian Scientific and Technical Conference. Moscow: NRNU MEPHI; 2011. P. 185 (in Russian).
  16. Nowak LG, Azouz R, Sanchez-Vives MV, Gray CM, McCormick DA. Electrophysiological classes of cat primary visual cortical neurons in vivo as revealed by quantitative analyses. J. Neurophysiol. 2003;89(3):1541–1566. DOI: 10.1152/jn.00580.2002.
  17. Orban GA. Neuronal Operations in the Visual Cortex. Studies of Brain Function. Berlin–Heidelberg; N-Y; Tokyo: Springer; 1984. 367 p. DOI: 10.1007/978-3-642-46469-0.
  18. Markin SN, Podladchikova LN, Dunin-Barkowski WL. Method to detect impulses of various duration generated by Purkinje cells of cerebellar cortex. Pattern Recognition and Image Analysis. 2005;15(4):672–675.
  19. Hodgkin A, Huxley A. A quantitative description of membrane current and its application to conduction and excitation in nerve. J. Physiol. 1952;117(4):500–544. DOI: 10.1113/jphysiol.1952.sp004764.
  20. Tikiji-Khamburyan RA. Modified impulse neuron as a basic model for real neural networks. Neurocomputers: Development and Application. 2002;(7–8):97 (in Russian).
  21. Wang XJ, Liu Y, Sanches-Vives MV, McCormick DA. Adaptation and temporal decorrelation by single neuron in the primary visual cortex. J. Neurophysiol. 2003;89(6):3279–3293. DOI: 10.1152/jn.00242.2003.
  22. Tikiji-Khamburyan RA. Modification of the genetic algorithm based on elite selection to search for parameters of biologically based neuron models. Neuroinformatics. 2008;3(1):1–12 (in Russian).
  23. Podladchikova LN, Tikidzhi-Khamburyan RA, Bondar GG, Ivlev SA, Dunin-Barkovsky VL. Features of periods of quasi-rhythmic activity of "fast" and "slow" neurons of the visual cortex and cerebellum: experiment and model. In: Proceedings of the XIV International Conference on Neurocybernetics, September 27-30, 2005. Vol.2 . Rostov-on-Don: Publishing House «CVUR»; 2005. 132 p. (in Russian),
  24. Anderson J, Lampl I, Reichova I, Carandini M, Ferster D. Stimulus dependence of two-state fluctuations of membrane potential in cat visual cortex. Nature Neuroscience. 2000;3(6):617–621. DOI: 10.1038/75797.
  25. Podladchikova LN, Bondar GG, Ivlev SA, Tikidzhi-Khamburyan RA, Dunin-Barkovsky VL. Dynamics of activity of cerebellar Purkinje cells when changing the duration of complex impulses. Biophysics. 2008;53(3):488–494 (in Russian).
  26. Helmstaedter M, de Kock CPJ, Feldmeyer D, Bruno RM, Sakmann B. Reconstruction of an average cortical column in silico. Brain Res. Rev. 2007;55(2):193–203. DOI: 10.1016/j.brainresrev.2007.07.011.
  27. The Blue Brain Project [Electronic resource]. Available from: http://bluebrain.epfl.ch.
  28. Thomson AM, Armstrong WE. Biocytin-labelling and its impact on late 20th century studies of cortical circuitry. Brain Res. Rev. 2011;66(1–2):43–53. DOI: 10.1016/j.brainresrev.2010.04.004.
  29. Silberberg G, Wu C, Markram H. Synaptic dynamics control the timing of neuronal excitation in the activated neocortical microcircuit. J. Physiol. 2004;556(1):19–27. DOI: 10.1113/jphysiol.2004.060962.
Received: 
13.07.2011
Accepted: 
13.07.2011
Published: 
29.02.2012
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