Linked Activity of Neurons in the Sensorimotor Cortex of the Rabbit in the State of a Defensive Dominant and “Animal Hypnosis”


A cryptic focus of excitation (a dominant focus) was created in the brains of rabbits by threshold stimulation of the left limb with a current at a frequency of 0.5 Hz. After creation of a focus, there were equal probabilities of detecting pairs of neurons whose linked activity was dominated by a 2-sec rhythm in the sensorimotor cortex of both the right and left hemispheres (29.3% and 32.4%, respectively). When animals were placed in “animal hypnosis,” the total proportion of neuron pairs whose activity was dominated by the rhythm created by establishment of the dominant decreased significantly only in the right hemisphere (21%). After exiting the state of animal hypnosis, the proportion of neurons in the cortex of the right hemisphere whose activity was dominated by the 2-sec rhythm increased significantly if the neurons in the pair were close-lying but decreased significantly if the neurons in the pair were mutually distant. No such changes after hypnotization were seen in the cortex of the left hemisphere. In both the right and left hemispheres, dominance of the 2-sec rhythm in the activity of pairs of neurons was seen significantly more frequently when cross-correlation histograms were constructed by analyzing cells in relation to the spike activity of neurons generating spikes of the lowest (right hemisphere) or lowest and intermediate (left hemisphere) amplitude on neurograms of multineuron activity.


rabbits dopamine multineuron activity neuronal codes 


  1. 1.
    A. V. Bogdanov and A. G. Galashina, “Transmission of encoded information through neuronal systems using the motor rhythmic dominant as an example,” Zh. Vyssh. Nerv. Deyat., 49, No. 6, 971–984 (1999).Google Scholar
  2. 2.
    A. V. Bogdanov and A. G. Galashina, “Analysis of linked spike activity in pairs of neurons in cerebral cortex microstructures,” Ros. Fiziol. Zh., 86, No. 5, 497–506 (2000).Google Scholar
  3. 3.
    A. V. Bogdanov, A. G. Galashina, and M. A. Kulikov, “The effects of ‘animal hypnosis’ on intersignal movements in a rhythmic defensive dominant,” Zh. Vyssh. Nerv. Deyat., 57, No. 2, 186–195 (2007).Google Scholar
  4. 4.
    P. V. Bukh-Viner, I. V. Volkov, and G. Kh. Merzhanova, “Spike ‘Collector’,” Zh. Vyssh. Nerv. Deyat., 40, No. 6, 1194–1199 (1990).Google Scholar
  5. 5.
    A. G. Galashina and A. V. Bogdanov, “Analysis of cat motor cortex neuron activity during a food-procuring reflex to time,” Zh. Vyssh. Nerv. Deyat., 37, No. 4, 766–768 (1987).Google Scholar
  6. 6.
    A. G. Galashina, M. A. Kulikov, and A. V. Bogdanov, “The effects of ‘animal hypnosis’ on a rhythmic defensive dominant,” Zh. Vyssh. Nerv. Deyat., 57, No. 1, 44–52 (2007).Google Scholar
  7. 7.
    U. G. Gasanov, “Gnostic micronetworks of neurons,” Zh. Vyssh. Nerv. Deyat., 37, No. 4, 634–641 (1987).Google Scholar
  8. 8.
    U. G. Gasanov, “Questions of internal inhibition,” Usp. Fiziol. Nauk., 19, No. 1, 66–87 (1988).PubMedGoogle Scholar
  9. 9.
    V. E. Amassian and M. Stewart, “Motor cortical and other cortical interneuronal networks that generate very high frequency waves,” Suppl. Clin. Neurophysiol., 56, 119–142 (2003).PubMedCrossRefGoogle Scholar
  10. 10.
    M. Brecht,W. Singer, and A. K. Engel, “Patterns of synchronization in the superior colliculus of anesthetized cats,” J. Neurosci., 19, No. 9, 3567–3579 (1999).PubMedGoogle Scholar
  11. 11.
    E. Y. Chang, K. F. Morris, R. Shannon, and B. G. J. Lindsey, “Repeated sequences of interspike intervals in baroresponsive respiratory related neuronal assemblies of the cat brain stem,” Neurophysiol., 84, No. 3, 1136–1148 (2000).Google Scholar
  12. 12.
    D. Chawla E. D. Lumer, and K. J. Friston, “The relationship between synchronization among neuronal populations and their mean activity levels,” Neural Comput., 11, No. 6, 1389–1411 (1999).PubMedCrossRefGoogle Scholar
  13. 13.
    S. R. Cobb, E. H. Buhl, K. Halasy, O. Paulsen, and P. Somogyi, “Synchronization of neuronal activity in hippocampus by individual GABAergic interneurons,” Nature, 378, No. 6552, 75–78 (1995).PubMedCrossRefGoogle Scholar
  14. 14.
    J. P. Donoghue, J. N. Sanes, N. G. Hatsopoulos, and G. Gaal, “Neural discharge and local field potential oscillations in primate motor cortex during voluntary movements,” J. Neurophysiol., 79, No. 1, 159–173 (1998).PubMedGoogle Scholar
  15. 15.
    P. M. Gochin, E. K. Miller, C. G. Gross, and G. L. Gerstein, “Functional interactions among neurons in inferior temporal cortex of the awake macaque,” Exptl. Brain Res., 84, No. 3, 505–516 (1991).CrossRefGoogle Scholar
  16. 16.
    P. M. Gochin, M. Colombo, G. A. Dorfman, G. L. Gerstein, and C. G. Gross, “Neural ensemble coding in inferior temporal cortex,” J. Neurophysiol., 71, No. 6, 2325–2337 (1994).PubMedGoogle Scholar
  17. 17.
    Y. Ikegaya, G. Aaron, R. Cosssart, D. Aronov, I. Lampl, D. Ferster, and R. Yuste, “Synfire chains and cortical songs: temporal modules of cortical activity: temporal modules of cortical activity,” Science, 304, No. 5670, 559–564 (2004).PubMedCrossRefGoogle Scholar
  18. 18.
    G. Kreiman, R. Krahe, W. Metzner, C. Koch, and F. Gabbiani, “Robustness and variability of neuronal coding by amplitude-sensitive afferents in the weakly electric fish Eigenmannia,” J. Neurophysiol., 84, No. 1, 189–204 (2000).PubMedGoogle Scholar
  19. 19.
    J. Kretzberg, A. K. Warzecha, and M. Egelhaaf, “Neural coding with graded membrane potential changes and spikes,” J. Comput. Neurosci., 11, No. 2, 153–164 (2001).PubMedCrossRefGoogle Scholar
  20. 20.
    R. Lestienne and H. C. Tuckwell, “The significance of precisely replicating pattern in mammalian CNS spike trains,” Neurosci., 82, No. 2, 315–336 (1998).CrossRefGoogle Scholar
  21. 21.
    P. E. Maldonado, S. Friedman-Hill, and C. M. Gray, “Dynamics of striate cortical activity in the alert macaque: II. Fast time scale synchronization. II. Fast time scale synchronization,” Cereb. Cortex, 10, No. 11, 1117–1131 (2000).PubMedCrossRefGoogle Scholar
  22. 22.
    Z. Nadasdy, “Spike sequences and their consequences,” J. Physiol. (France), 94, No. 5–6, 505–524 (2000).Google Scholar
  23. 23.
    S. Nirenberg and P. E. Latham, “Decoding neuronal spike trains: how important are correlations?” Proc. Nat. Acad. Sci. USA, 100, No. 12, 7348–7353 (2003).PubMedCrossRefGoogle Scholar
  24. 24.
    M. Raastad and O. Kiehn, “Spike coding during locomotor network activity in ventrally located neurons in the isolated spinal cord from neonatal rat,” J. Neurophysiol., 83, No. 5, 2825–2834 (2000).PubMedGoogle Scholar
  25. 25.
    D. S. Reich, J. D. Victor, B. W. Knight, T. Ozaki, and E. Kaplan, “Response variability and timing precision of neuronal spike trains in vivo,” J. Neurophysiol., 77, No. 5, 2836–2841 (1997).PubMedGoogle Scholar
  26. 26.
    F. S. Roman, B. Truchet, F. A. Chaillan, E. Marchetti, and B. Soumireu-Mourat, “Olfactory associative discrimination: a model for studying modifications of synaptic efficacy in neuronal networks supporting long-term memory,” Rev. Neurosci., 15, No. 1, 1–17 (2004).PubMedGoogle Scholar
  27. 27.
    R. Romo, A. Hernandez, A. Zainos, and E. Salinas, “Correlated neuronal discharges that increase coding efficiency during perceptual discrimination,” Neuron, 38, No. 4, 649–657 (2003).PubMedCrossRefGoogle Scholar
  28. 28.
    Y. Shu, A. Hasenstaug, and D. A. McCormick, “Turning on and off recurrent balanced cortical activity,” Nature, 423, No. 6937, 288–293 (2003).PubMedCrossRefGoogle Scholar
  29. 29.
    I. V. Tetko and A. E. Villa, “A pattern grouping algorithm for analysis of spatiotemporal patterns in neuronal spike trains. 2. Application to simultaneous single unit recordings,” J. Neurosci. Meth., 105, No. 1, 15–24 (2001).CrossRefGoogle Scholar
  30. 30.
    P. H. Tiesinga and T. J. Sejnowski, “Rapid temporal modulation of synchrony by competition in cortical interneuron networks,” Neural Comput., 16, No. 2, 251–275 (2004).PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, Inc. 2009

Authors and Affiliations

  1. 1.Institute of Higher Nervous Activity and NeurophysiologyRussian Academy of SciencesMoscowRussia

Personalised recommendations