• DocumentCode
    380564
  • Title

    Chaotic and statistical analysis of multiple unit neuronal activities on the learning stimuli

  • Author

    Cho, S.Y. ; Kim, B.Y. ; Jang, Y.S. ; Kim, H.T.

  • Author_Institution
    Dept. of Neurosci., Kyung Hee Univ., Seoul, South Korea
  • Volume
    1
  • fYear
    2001
  • fDate
    2001
  • Abstract
    Summary form only given. We are to apply the chaotic features in the analysis of neuronal activity recorded from brain area involved in learning. Neural plasticity during learning is a major theme in the neurobiology of learning and memory. We recorded the multiple-unit activities at four brain sites that involve the classical conditioning of rabbits´ nictitating membrane responses. The neuronal data were analyzed using chaotic features including the box-counting dimension, fractal dimension and central tendency, as well as statistical features. The chaotic characteristics represent the change of neuronal response by learning. Each index showed the change within a trial, the change across the training sessions, and the differences between the trials showing the learned responses and the trials not showing. The chaotic characteristics could be useful indices in the representation of the neural plasticity.
  • Keywords
    brain models; chaos; fractals; neurophysiology; statistical analysis; box-counting dimension; central tendency; chaotic analysis; classical conditioning; fractal dimension; learning stimuli; memory; multiple unit activity; neurobiology; neuronal plasticity; rabbits´ nictitating membrane responses; statical analysis; statistical features; Biomedical engineering; Biomembranes; Brain; Chaos; Data analysis; Fractals; Psychology; Rabbits; Statistics;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Engineering in Medicine and Biology Society, 2001. Proceedings of the 23rd Annual International Conference of the IEEE
  • ISSN
    1094-687X
  • Print_ISBN
    0-7803-7211-5
  • Type

    conf

  • DOI
    10.1109/IEMBS.2001.1019073
  • Filename
    1019073