IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v14y2023i1d10.1038_s41467-023-42553-3.html
   My bibliography  Save this article

Change detection in the primate auditory cortex through feedback of prediction error signals

Author

Listed:
  • Keitaro Obara

    (The University of Tokyo
    RIKEN Center for Brain Science)

  • Teppei Ebina

    (The University of Tokyo)

  • Shin-Ichiro Terada

    (The University of Tokyo)

  • Takanori Uka

    (University of Yamanashi)

  • Misako Komatsu

    (RIKEN Center for Brain Science)

  • Masafumi Takaji

    (RIKEN Center for Brain Science
    RIKEN Center for Brain Science)

  • Akiya Watakabe

    (RIKEN Center for Brain Science
    RIKEN Center for Brain Science)

  • Kenta Kobayashi

    (Section of Viral Vector Development, National Institute for Physiological Sciences)

  • Yoshito Masamizu

    (RIKEN Center for Brain Science)

  • Hiroaki Mizukami

    (Jichi Medical University)

  • Tetsuo Yamamori

    (RIKEN Center for Brain Science
    RIKEN Center for Brain Science
    Central Institute of Experimental Animals)

  • Kiyoto Kasai

    (The University of Tokyo
    The University of Tokyo Institutes for Advanced Study)

  • Masanori Matsuzaki

    (The University of Tokyo
    RIKEN Center for Brain Science
    The University of Tokyo Institutes for Advanced Study)

Abstract

Although cortical feedback signals are essential for modulating feedforward processing, no feedback error signal across hierarchical cortical areas has been reported. Here, we observed such a signal in the auditory cortex of awake common marmoset during an oddball paradigm to induce auditory duration mismatch negativity. Prediction errors to a deviant tone presentation were generated as offset calcium responses of layer 2/3 neurons in the rostral parabelt (RPB) of higher-order auditory cortex, while responses to non-deviant tones were strongly suppressed. Within several hundred milliseconds, the error signals propagated broadly into layer 1 of the primary auditory cortex (A1) and accumulated locally on top of incoming auditory signals. Blockade of RPB activity prevented deviance detection in A1. Optogenetic activation of RPB following tone presentation nonlinearly enhanced A1 tone response. Thus, the feedback error signal is critical for automatic detection of unpredicted stimuli in physiological auditory processing and may serve as backpropagation-like learning.

Suggested Citation

  • Keitaro Obara & Teppei Ebina & Shin-Ichiro Terada & Takanori Uka & Misako Komatsu & Masafumi Takaji & Akiya Watakabe & Kenta Kobayashi & Yoshito Masamizu & Hiroaki Mizukami & Tetsuo Yamamori & Kiyoto , 2023. "Change detection in the primate auditory cortex through feedback of prediction error signals," Nature Communications, Nature, vol. 14(1), pages 1-17, December.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-42553-3
    DOI: 10.1038/s41467-023-42553-3
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-023-42553-3
    File Function: Abstract
    Download Restriction: no
    File URL: https://libkey.io/10.1038/s41467-023-42553-3?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
    ---><---

    References listed on IDEAS

    as
    1. Gloria G. Parras & Javier Nieto-Diego & Guillermo V. Carbajal & Catalina Valdés-Baizabal & Carles Escera & Manuel S. Malmierca, 2017. "Neurons along the auditory pathway exhibit a hierarchical organization of prediction error," Nature Communications, Nature, vol. 8(1), pages 1-17, December.
    2. Teppei Ebina & Yoshito Masamizu & Yasuhiro R. Tanaka & Akiya Watakabe & Reiko Hirakawa & Yuka Hirayama & Riichiro Hira & Shin-Ichiro Terada & Daisuke Koketsu & Kazuo Hikosaka & Hiroaki Mizukami & Atsu, 2018. "Two-photon imaging of neuronal activity in motor cortex of marmosets during upper-limb movement tasks," Nature Communications, Nature, vol. 9(1), pages 1-16, December.
    3. Debajit Saha & Wensheng Sun & Chao Li & Srinath Nizampatnam & William Padovano & Zhengdao Chen & Alex Chen & Ege Altan & Ray Lo & Dennis L. Barbour & Baranidharan Raman, 2017. "Engaging and disengaging recurrent inhibition coincides with sensing and unsensing of a sensory stimulus," Nature Communications, Nature, vol. 8(1), pages 1-19, August.
    4. Timothy P. Lillicrap & Daniel Cownden & Douglas B. Tweed & Colin J. Akerman, 2016. "Random synaptic feedback weights support error backpropagation for deep learning," Nature Communications, Nature, vol. 7(1), pages 1-10, December.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. Shinnosuke Nomura & Shin-Ichiro Terada & Teppei Ebina & Masato Uemura & Yoshito Masamizu & Kenichi Ohki & Masanori Matsuzaki, 2024. "ARViS: a bleed-free multi-site automated injection robot for accurate, fast, and dense delivery of virus to mouse and marmoset cerebral cortex," Nature Communications, Nature, vol. 15(1), pages 1-23, December.
    2. Teppei Ebina & Akitaka Sasagawa & Dokyeong Hong & Rieko Setsuie & Keitaro Obara & Yoshito Masamizu & Masashi Kondo & Shin-Ichiro Terada & Katsuya Ozawa & Masato Uemura & Masafumi Takaji & Akiya Wataka, 2024. "Dynamics of directional motor tuning in the primate premotor and primary motor cortices during sensorimotor learning," Nature Communications, Nature, vol. 15(1), pages 1-21, December.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Antonino Greco & Julia Moser & Hubert Preissl & Markus Siegel, 2024. "Predictive learning shapes the representational geometry of the human brain," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    2. Giorgia Dellaferrera & Stanisław Woźniak & Giacomo Indiveri & Angeliki Pantazi & Evangelos Eleftheriou, 2022. "Introducing principles of synaptic integration in the optimization of deep neural networks," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    3. Frank Gelens & Juho Äijälä & Louis Roberts & Misako Komatsu & Cem Uran & Michael A. Jensen & Kai J. Miller & Robin A. A. Ince & Max Garagnani & Martin Vinck & Andres Canales-Johnson, 2024. "Distributed representations of prediction error signals across the cortical hierarchy are synergistic," Nature Communications, Nature, vol. 15(1), pages 1-18, December.
    4. Mitsumasa Nakajima & Katsuma Inoue & Kenji Tanaka & Yasuo Kuniyoshi & Toshikazu Hashimoto & Kohei Nakajima, 2022. "Physical deep learning with biologically inspired training method: gradient-free approach for physical hardware," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    5. Ertam, Fatih, 2019. "An efficient hybrid deep learning approach for internet security," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 535(C).
    6. Shinnosuke Nomura & Shin-Ichiro Terada & Teppei Ebina & Masato Uemura & Yoshito Masamizu & Kenichi Ohki & Masanori Matsuzaki, 2024. "ARViS: a bleed-free multi-site automated injection robot for accurate, fast, and dense delivery of virus to mouse and marmoset cerebral cortex," Nature Communications, Nature, vol. 15(1), pages 1-23, December.
    7. Robert Rosenbaum, 2022. "On the relationship between predictive coding and backpropagation," PLOS ONE, Public Library of Science, vol. 17(3), pages 1-27, March.
    8. Navid Shervani-Tabar & Robert Rosenbaum, 2023. "Meta-learning biologically plausible plasticity rules with random feedback pathways," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    9. Teppei Ebina & Akitaka Sasagawa & Dokyeong Hong & Rieko Setsuie & Keitaro Obara & Yoshito Masamizu & Masashi Kondo & Shin-Ichiro Terada & Katsuya Ozawa & Masato Uemura & Masafumi Takaji & Akiya Wataka, 2024. "Dynamics of directional motor tuning in the primate premotor and primary motor cortices during sensorimotor learning," Nature Communications, Nature, vol. 15(1), pages 1-21, December.
    10. Rishabh Chandak & Baranidharan Raman, 2023. "Neural manifolds for odor-driven innate and acquired appetitive preferences," Nature Communications, Nature, vol. 14(1), pages 1-21, December.
    11. Alpha Renner & Forrest Sheldon & Anatoly Zlotnik & Louis Tao & Andrew Sornborger, 2024. "The backpropagation algorithm implemented on spiking neuromorphic hardware," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    12. Alexander Ororbia & Daniel Kifer, 2022. "The neural coding framework for learning generative models," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    13. Jimin Wu & Yuzhi Chen & Ashok Veeraraghavan & Eyal Seidemann & Jacob T. Robinson, 2024. "Mesoscopic calcium imaging in a head-unrestrained male non-human primate using a lensless microscope," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    14. Stefano Recanatesi & Gabriel Koch Ocker & Michael A Buice & Eric Shea-Brown, 2019. "Dimensionality in recurrent spiking networks: Global trends in activity and local origins in connectivity," PLOS Computational Biology, Public Library of Science, vol. 15(7), pages 1-29, July.
    15. Michele N. Insanally & Badr F. Albanna & Jade Toth & Brian DePasquale & Saba Shokat Fadaei & Trisha Gupta & Olivia Lombardi & Kishore Kuchibhotla & Kanaka Rajan & Robert C. Froemke, 2024. "Contributions of cortical neuron firing patterns, synaptic connectivity, and plasticity to task performance," Nature Communications, Nature, vol. 15(1), pages 1-21, December.

    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:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-42553-3. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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.