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Synaptic depression enables neuronal gain control

Author

Listed:
  • Jason S. Rothman

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

  • Laurence Cathala

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

  • Volker Steuber

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

  • R. Angus Silver

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

Abstract

Higher mathematics for neurons Neurons are computational devices that perform mathematical operations such as additions, with their firing rate (or output) representing the sum of their synaptic conductances (or input). Multiplication, in turn, can result from changes in the slope - or gain - of such input-output relationship. Such changes in a neuron's sensitivity result from neuromodulation and are key to numerous higher brain computations, such as the visual system's ability to detect object orientation whatever the contrast. But mechanisms underlying neuronal gain modulation have been unclear. A new study in cerebellum demonstrates that short-term synaptic plasticity brings the fundamental nonlinearity, allowing neuromodulatory inhibition to act multiplicatively instead of additively.

Suggested Citation

  • Jason S. Rothman & Laurence Cathala & Volker Steuber & R. Angus Silver, 2009. "Synaptic depression enables neuronal gain control," Nature, Nature, vol. 457(7232), pages 1015-1018, February.
    • Handle: RePEc:nat:nature:v:457:y:2009:i:7232:d:10.1038_nature07604
    DOI: 10.1038/nature07604
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    Cited by:

    1. Amanda M. Perozzo & Jochen Schwenk & Aichurok Kamalova & Terunaga Nakagawa & Bernd Fakler & Derek Bowie, 2023. "GSG1L-containing AMPA receptor complexes are defined by their spatiotemporal expression, native interactome and allosteric sites," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
    2. Robert Rosenbaum & Jonathan Rubin & Brent Doiron, 2012. "Short Term Synaptic Depression Imposes a Frequency Dependent Filter on Synaptic Information Transfer," PLOS Computational Biology, Public Library of Science, vol. 8(6), pages 1-18, June.
    3. Joshua H Goldwyn & Bradley R Slabe & Joseph B Travers & David Terman, 2018. "Gain control with A-type potassium current: IA as a switch between divisive and subtractive inhibition," PLOS Computational Biology, Public Library of Science, vol. 14(7), pages 1-23, July.
    4. A. Barri & M. T. Wiechert & M. Jazayeri & D. A. DiGregorio, 2022. "Synaptic basis of a sub-second representation of time in a neural circuit model," Nature Communications, Nature, vol. 13(1), pages 1-18, December.
    5. Corentin Massot & Adam D Schneider & Maurice J Chacron & Kathleen E Cullen, 2012. "The Vestibular System Implements a Linear–Nonlinear Transformation In Order to Encode Self-Motion," PLOS Biology, Public Library of Science, vol. 10(7), pages 1-20, July.

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