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Visual input evokes transient and strong shunting inhibition in visual cortical neurons

Author

Listed:
  • Lyle J. Borg-Graham

    (Equipe Cognisciences, Institut Alfred Fessard, CNRS)

  • Cyril Monier

    (Equipe Cognisciences, Institut Alfred Fessard, CNRS)

  • Yves Frégnac

    (Equipe Cognisciences, Institut Alfred Fessard, CNRS)

Abstract

The function and nature of inhibition of neurons in the visual cortex have been the focus of both experimental and theoretical investigations1,2,3,4,5,6,7. There are two ways in which inhibition can suppress synaptic excitation2,8. In hyperpolarizing inhibition, negative and positive currents sum linearly to produce a net change in membrane potential. In contrast, shunting inhibition acts nonlinearly by causing an increase in membrane conductance; this divides the amplitude of the excitatory response. Visually evoked changes in membrane conductance have been reported to be nonsignificant or weak, supporting the hyperpolarization mode of inhibition3,9,10,11,12. Here we present a new approach to studying inhibition that is based on in vivo whole-cell voltage clamping. This technique allows the continuous measurement of conductance dynamics during visual activation. We show, in neurons of cat primary visual cortex, that the response to optimally orientated flashed bars can increase the somatic input conductance to more than three times that of the resting state. The short latency of the visually evoked peak of conductance, and its apparent reversal potential suggest a dominant contribution from γ-aminobutyric acid ((GABA)A) receptor-mediated synapses. We propose that nonlinear shunting inhibition may act during the initial stage of visual cortical processing, setting the balance between opponent ‘On’ and ‘Off’ responses in different locations of the visual receptive field.

Suggested Citation

  • Lyle J. Borg-Graham & Cyril Monier & Yves Frégnac, 1998. "Visual input evokes transient and strong shunting inhibition in visual cortical neurons," Nature, Nature, vol. 393(6683), pages 369-373, May.
  • Handle: RePEc:nat:nature:v:393:y:1998:i:6683:d:10.1038_30735
    DOI: 10.1038/30735
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    Cited by:

    1. Catalina Vich & Rafel Prohens & Antonio E. Teruel & Antoni Guillamon, 2020. "Estimation of Synaptic Activity during Neuronal Oscillations," Mathematics, MDPI, vol. 8(12), pages 1-22, December.
    2. Valentin Markounikau & Christian Igel & Amiram Grinvald & Dirk Jancke, 2010. "A Dynamic Neural Field Model of Mesoscopic Cortical Activity Captured with Voltage-Sensitive Dye Imaging," PLOS Computational Biology, Public Library of Science, vol. 6(9), pages 1-14, September.
    3. Matteo Farinella & Daniel T Ruedt & Padraig Gleeson & Frederic Lanore & R Angus Silver, 2014. "Glutamate-Bound NMDARs Arising from In Vivo-like Network Activity Extend Spatio-temporal Integration in a L5 Cortical Pyramidal Cell Model," PLOS Computational Biology, Public Library of Science, vol. 10(4), pages 1-21, April.
    4. Ruxandra Barzan & Beyza Bozkurt & Mohammadreza M. Nejad & Sandra T. Süß & Tatjana Surdin & Hanna Böke & Katharina Spoida & Zohre Azimi & Michelle Grömmke & Dennis Eickelbeck & Melanie D. Mark & Lennar, 2024. "Gain control of sensory input across polysynaptic circuitries in mouse visual cortex by a single G protein-coupled receptor type (5-HT2A)," Nature Communications, Nature, vol. 15(1), pages 1-14, December.

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