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Unique features of action potential initiation in cortical neurons

Author

Listed:
  • Björn Naundorf

    (Max Planck Institute for Dynamics and Self-Organization
    Department of Physics
    University of Göttingen)

  • Fred Wolf

    (Max Planck Institute for Dynamics and Self-Organization
    Department of Physics
    University of Göttingen)

  • Maxim Volgushev

    (Ruhr-University Bochum
    Institute of Higher Nervous Activity and Neurophysiology Russian Academy of Sciences)

Abstract

Quick thinking Neurons in the brain communicate via electric pulses or action potentials of a fraction of a volt, that last about a thousandth of a second. In 1952, Alan Hodgkin and Andrew Huxley received a share in the Nobel prize for their theory of action potential generation, developed from work on the squid giant axon. Ever since it has been tacitly assumed that nerve impulses are generated in much the same way in all animals from slugs to humans. Now a study of the cortical neurons of higher animals suggests that this assumption may need to be revised. Key features of cortical action potential initiation depart from predictions of the Hodgkin–Huxley theory, as the neurons are much more tailored for fast information processing than was assumed.

Suggested Citation

  • Björn Naundorf & Fred Wolf & Maxim Volgushev, 2006. "Unique features of action potential initiation in cortical neurons," Nature, Nature, vol. 440(7087), pages 1060-1063, April.
    • Handle: RePEc:nat:nature:v:440:y:2006:i:7087:d:10.1038_nature04610
    DOI: 10.1038/nature04610
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    Citations

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    Cited by:

    1. Skander Mensi & Olivier Hagens & Wulfram Gerstner & Christian Pozzorini, 2016. "Enhanced Sensitivity to Rapid Input Fluctuations by Nonlinear Threshold Dynamics in Neocortical Pyramidal Neurons," PLOS Computational Biology, Public Library of Science, vol. 12(2), pages 1-38, February.
    2. Cristina Rueda & Itziar Fernández & Yolanda Larriba & Alejandro Rodríguez-Collado, 2021. "The FMM Approach to Analyze Biomedical Signals: Theory, Software, Applications and Future," Mathematics, MDPI, vol. 9(10), pages 1-13, May.
    3. Robert C Cannon & Giampaolo D'Alessandro, 2006. "The Ion Channel Inverse Problem: Neuroinformatics Meets Biophysics," PLOS Computational Biology, Public Library of Science, vol. 2(8), pages 1-8, August.
    4. Lucy J Colwell & Michael P Brenner, 2009. "Action Potential Initiation in the Hodgkin-Huxley Model," PLOS Computational Biology, Public Library of Science, vol. 5(1), pages 1-7, January.
    5. Lior Tiroshi & Joshua A Goldberg, 2019. "Population dynamics and entrainment of basal ganglia pacemakers are shaped by their dendritic arbors," PLOS Computational Biology, Public Library of Science, vol. 15(2), pages 1-29, February.
    6. Paul M Harrison & Laurent Badel & Mark J Wall & Magnus J E Richardson, 2015. "Experimentally Verified Parameter Sets for Modelling Heterogeneous Neocortical Pyramidal-Cell Populations," PLOS Computational Biology, Public Library of Science, vol. 11(8), pages 1-23, August.
    7. Contoyiannis, Yiannis F. & Kosmidis, Efstratios K. & Diakonos, Fotios K. & Kampitakis, Myron & Potirakis, Stelios M., 2022. "A hybrid artificial neural network for the generation of critical fluctuations and inter-spike intervals," Chaos, Solitons & Fractals, Elsevier, vol. 159(C).
    8. Jonathan Platkiewicz & Romain Brette, 2010. "A Threshold Equation for Action Potential Initiation," PLOS Computational Biology, Public Library of Science, vol. 6(7), pages 1-16, July.
    9. Ahmed A Aldohbeyb & Jozsef Vigh & Kevin L Lear, 2021. "New methods for quantifying rapidity of action potential onset differentiate neuron types," PLOS ONE, Public Library of Science, vol. 16(4), pages 1-20, April.

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