IDEAS home Printed from https://ideas.repec.org/a/nat/nature/v466y2010i7302d10.1038_nature09086.html
   My bibliography  Save this article

Sensitivity to perturbations in vivo implies high noise and suggests rate coding in cortex

Author

Listed:
  • Michael London

    (Physiology and Pharmacology, University College London, Gower Street, London WC1E 6BT, UK)

  • Arnd Roth

    (Physiology and Pharmacology, University College London, Gower Street, London WC1E 6BT, UK)

  • Lisa Beeren

    (Physiology and Pharmacology, University College London, Gower Street, London WC1E 6BT, UK)

  • Michael Häusser

    (Physiology and Pharmacology, University College London, Gower Street, London WC1E 6BT, UK)

  • Peter E. Latham

    (Gatsby Computational Neuroscience Unit, University College London, Queen Square, London WC1N 3AR, UK)

Abstract

Neural coding: no time for noise Neural responses are variable — identical sensory stimuli produce different responses — but it is not clear whether this variability carries important information, or whether it is just noise. London et al. characterize the sensitivity to small fluctuations of in vivo cortical networks in rat barrel cortex in the context of their consequences for neural coding. A perturbation equivalent to adding a single spike in one neuron produces about 28 additional spikes in its projection targets and a detectable increase in local firing rate. Simulations suggest that this amplification leads to large intrinsic variations in the system that are pure noise, carrying no information about the input, and therefore unsuited for carrying a reliable temporal code. The authors conclude that rat barrel cortex is likely to use primarily a rate code.

Suggested Citation

  • Michael London & Arnd Roth & Lisa Beeren & Michael Häusser & Peter E. Latham, 2010. "Sensitivity to perturbations in vivo implies high noise and suggests rate coding in cortex," Nature, Nature, vol. 466(7302), pages 123-127, July.
    • Handle: RePEc:nat:nature:v:466:y:2010:i:7302:d:10.1038_nature09086
    DOI: 10.1038/nature09086
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/nature09086
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1038/nature09086?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Omri Harish & David Hansel, 2015. "Asynchronous Rate Chaos in Spiking Neuronal Circuits," PLOS Computational Biology, Public Library of Science, vol. 11(7), pages 1-38, July.
    2. Christian Meisel & Andreas Klaus & Christian Kuehn & Dietmar Plenz, 2015. "Critical Slowing Down Governs the Transition to Neuron Spiking," PLOS Computational Biology, Public Library of Science, vol. 11(2), pages 1-20, February.
    3. Evan S Schaffer & Srdjan Ostojic & L F Abbott, 2013. "A Complex-Valued Firing-Rate Model That Approximates the Dynamics of Spiking Networks," PLOS Computational Biology, Public Library of Science, vol. 9(10), pages 1-11, October.
    4. Yasuhiro Tsubo & Yoshikazu Isomura & Tomoki Fukai, 2012. "Power-Law Inter-Spike Interval Distributions Infer a Conditional Maximization of Entropy in Cortical Neurons," PLOS Computational Biology, Public Library of Science, vol. 8(4), pages 1-11, April.
    5. Volker Pernice & Benjamin Staude & Stefano Cardanobile & Stefan Rotter, 2011. "How Structure Determines Correlations in Neuronal Networks," PLOS Computational Biology, Public Library of Science, vol. 7(5), pages 1-14, May.
    6. Vladimir Ilin & Ian H Stevenson & Maxim Volgushev, 2014. "Injection of Fully-Defined Signal Mixtures: A Novel High-Throughput Tool to Study Neuronal Encoding and Computations," PLOS ONE, Public Library of Science, vol. 9(10), pages 1-10, October.
    7. Matthias Schultze-Kraft & Markus Diesmann & Sonja Grün & Moritz Helias, 2013. "Noise Suppression and Surplus Synchrony by Coincidence Detection," PLOS Computational Biology, Public Library of Science, vol. 9(4), pages 1-15, April.
    8. Ravi Pancholi & Lauren Ryan & Simon Peron, 2023. "Learning in a sensory cortical microstimulation task is associated with elevated representational stability," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    9. Emiliano Torre & Carlos Canova & Michael Denker & George Gerstein & Moritz Helias & Sonja Grün, 2016. "ASSET: Analysis of Sequences of Synchronous Events in Massively Parallel Spike Trains," PLOS Computational Biology, Public Library of Science, vol. 12(7), pages 1-34, July.
    10. Mizusaki, Beatriz E.P. & Agnes, Everton J. & Erichsen, Rubem & Brunnet, Leonardo G., 2017. "Learning and retrieval behavior in recurrent neural networks with pre-synaptic dependent homeostatic plasticity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 479(C), pages 279-286.
    11. Angulo-Garcia, David & Torcini, Alessandro, 2014. "Stable chaos in fluctuation driven neural circuits," Chaos, Solitons & Fractals, Elsevier, vol. 69(C), pages 233-245.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:nature:v:466:y:2010:i:7302:d:10.1038_nature09086. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.