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Recurrent interactions in local cortical circuits

Author

Listed:
  • Simon Peron

    (Howard Hughes Medical Institute
    New York University)

  • Ravi Pancholi

    (New York University)

  • Bettina Voelcker

    (New York University)

  • Jason D. Wittenbach

    (Howard Hughes Medical Institute)

  • H. Freyja Ólafsdóttir

    (Howard Hughes Medical Institute
    University College London
    Radboud University)

  • Jeremy Freeman

    (Howard Hughes Medical Institute)

  • Karel Svoboda

    (Howard Hughes Medical Institute)

Abstract

Most cortical synapses are local and excitatory. Local recurrent circuits could implement amplification, allowing pattern completion and other computations1–4. Cortical circuits contain subnetworks that consist of neurons with similar receptive fields and increased connectivity relative to the network average5,6. Cortical neurons that encode different types of information are spatially intermingled and distributed over large brain volumes5–7, and this complexity has hindered attempts to probe the function of these subnetworks by perturbing them individually8. Here we use computational modelling, optical recordings and manipulations to probe the function of recurrent coupling in layer 2/3 of the mouse vibrissal somatosensory cortex during active tactile discrimination. A neural circuit model of layer 2/3 revealed that recurrent excitation enhances sensory signals by amplification, but only for subnetworks with increased connectivity. Model networks with high amplification were sensitive to damage: loss of a few members of the subnetwork degraded stimulus encoding. We tested this prediction by mapping neuronal selectivity7 and photoablating9,10 neurons with specific selectivity. Ablation of a small proportion of layer 2/3 neurons (10–20, less than 5% of the total) representing touch markedly reduced responses in the spared touch representation, but not in other representations. Ablations most strongly affected neurons with stimulus responses that were similar to those of the ablated population, which is also consistent with network models. Recurrence among cortical neurons with similar selectivity therefore drives input-specific amplification during behaviour.

Suggested Citation

  • Simon Peron & Ravi Pancholi & Bettina Voelcker & Jason D. Wittenbach & H. Freyja Ólafsdóttir & Jeremy Freeman & Karel Svoboda, 2020. "Recurrent interactions in local cortical circuits," Nature, Nature, vol. 579(7798), pages 256-259, March.
  • Handle: RePEc:nat:nature:v:579:y:2020:i:7798:d:10.1038_s41586-020-2062-x
    DOI: 10.1038/s41586-020-2062-x
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    Citations

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    Cited by:

    1. Bettina Voelcker & Ravi Pancholi & Simon Peron, 2022. "Transformation of primary sensory cortical representations from layer 4 to layer 2," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    2. 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.
    3. Xin Wei Chia & Jian Kwang Tan & Lee Fang Ang & Tsukasa Kamigaki & Hiroshi Makino, 2023. "Emergence of cortical network motifs for short-term memory during learning," Nature Communications, Nature, vol. 14(1), pages 1-17, December.
    4. Mohamad Motaharinia & Kim Gerrow & Roobina Boghozian & Emily White & Sun-Eui Choi & Kerry R. Delaney & Craig E. Brown, 2021. "Longitudinal functional imaging of VIP interneurons reveals sup-population specific effects of stroke that are rescued with chemogenetic therapy," Nature Communications, Nature, vol. 12(1), pages 1-17, December.
    5. Hyunsoo Yim & Daniel T. Choe & J. Alexander Bae & Myung-kyu Choi & Hae-Mook Kang & Ken C. Q. Nguyen & Soungyub Ahn & Sang-kyu Bahn & Heeseung Yang & David H. Hall & Jinseop S. Kim & Junho Lee, 2024. "Comparative connectomics of dauer reveals developmental plasticity," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    6. Shinichiro Kira & Houman Safaai & Ari S. Morcos & Stefano Panzeri & Christopher D. Harvey, 2023. "A distributed and efficient population code of mixed selectivity neurons for flexible navigation decisions," Nature Communications, Nature, vol. 14(1), pages 1-28, December.

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