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

Author

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  • 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
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    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. Angulo-Garcia, David & Torcini, Alessandro, 2014. "Stable chaos in fluctuation driven neural circuits," Chaos, Solitons & Fractals, Elsevier, vol. 69(C), pages 233-245.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.

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