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Computational Intelligence in Electrophysiology: Trends and Open Problems

  • Chapter
Applications of Computational Intelligence in Biology

Part of the book series: Studies in Computational Intelligence ((SCI,volume 122))

Summary

This chapter constitutes mini-proceedings of the Workshop on Physiology Databases and Analysis Software that was a part of the Annual Computational Neuroscience Meeting CNS*2007 that took place in July 2007 in Toronto, Canada (http ://www.cnsorg.org). The main aim of the workshop was to bring together researchers interested in developing and using automated analysis tools and database systems for electrophysiological data. Selected discussed topics, including the review of some current and potential applications of Computational Intelligence (CI) in electrophysiology, database and electrophysiological data exchange platforms, languages, and formats, as well as exemplary analysis problems, are presented in this chapter. The authors hope that the chapter will be useful not only to those already involved in the field of electrophysiology, but also to CI researchers, whose interest will be sparked by its contents.

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Authors and Affiliations

  1. Dept. of Biology, Emory University, Atlanta, Georgia, 30322, USA

    Cengiz Günay & Tomasz G. Smolinski

  2. Depts of Physiology /Pharmacology and Neurology, State University of New York - Downstate, Brooklyn, New York, 11203, USA

    William W. Lytton

  3. Dept. of Neurobiology, Yale University, New Haven, CT, 06510, USA

    Thomas M. Morse

  4. Dept. of Physiology, University College London, London, UK

    Padraig Gleeson, Volker Steuber & Angus Silver

  5. Dept. of Mathematics and Statistics, Arizona State University, Tempe, Arizona, USA

    Sharon Crook

  6. School of Computer Science, University of Hertfordshire, Hatfield, Herts, UK

    Volker Steuber

  7. Dept. of Neurobiology and Anatomy, University of Texas Health Science Center, Houston, TX, 77030, USA

    Horatiu Voicu

  8. Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, 11724, USA

    Peter Andrews, Hemant Bokil, Hiren Maniar & Partha P. Mitra

  9. Dept. of Statistics, University of Auckland, Auckland, 1142, New Zealand

    Catherine Loader

  10. School of Medicine, State University of New York - Downstate, Brooklyn, New York, 11203, USA

    Samar Mehta

  11. Dept. of Physics, University of California, San Diego, La Jolla, CA, 92093, USA

    David Kleinfeld

  12. Dept. of Mathematics and Statistics, Queen’s University, Kingston, ON, Canada

    David Thomson

  13. Dept. of Biology, Wesleyan University, Middletown, CT, 06459, USA

    Gloster Aaron

  14. Dept. of Psychology, University of Arizona, Tucson, AZ, 85721, USA

    Jean-Marc Fellous

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  1. Cengiz Günay
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  19. Jean-Marc Fellous
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Editors and Affiliations

  1. Department of Biology, Emory University, 1510 Clifton Road NE, Atlanta, Georgia, 30322, USA

    Tomasz G. Smolinski

  2. Department of Computer Science, University of Arkansas at Little Rock, 2801 S. University Ave., Little Rock, Arkansas, 72204, USA

    Mariofanna G. Milanova

  3. Department of Quantitative Methods and Information Systems College of Business and Administration, Kuwait University, P.O. Box 5486, Safat, 13055, Kuwait

    Aboul-Ella Hassanien

  4. Department of Information Technology Faculty of Computers and Information, Cairo University, 5 Ahamed Zewal Street, Orman, Giza, Egypt

    Aboul-Ella Hassanien

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Günay, C. et al. (2008). Computational Intelligence in Electrophysiology: Trends and Open Problems. In: Smolinski, T.G., Milanova, M.G., Hassanien, AE. (eds) Applications of Computational Intelligence in Biology. Studies in Computational Intelligence, vol 122. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78534-7_14

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