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Invariant visual representation by single neurons in the human brain

Author

Listed:
  • R. Quian Quiroga

    (California Institute of Technology
    University of California, (UCLA)
    University of Leicester)

  • L. Reddy

    (California Institute of Technology)

  • G. Kreiman

    (Massachusetts Institute of Technology)

  • C. Koch

    (California Institute of Technology)

  • I. Fried

    (University of California, (UCLA)
    Tel-Aviv University)

Abstract

Stars in their eyes It takes moments for the human brain to recognize a person or an object even if seen under very different conditions. This raises the question: can a single neuron respond selectively to a given face regardless of view, age, pose or context? That question — it has been called the search for the ‘grandmother neuron’ — is difficult to test. But now, in patients with intractable epilepsy who were implanted with depth electrodes for a clinical process, an answer has been obtained. Patients were asked to respond to images on computer screens, and the results showed that neurons are pretty single-minded in what they respond to. For instance, one neuron will respond selectively to different pictures of the actress Jennifer Aniston, one to basketball player Michael Jordan, and another to different views of the Tower of Pisa.

Suggested Citation

  • R. Quian Quiroga & L. Reddy & G. Kreiman & C. Koch & I. Fried, 2005. "Invariant visual representation by single neurons in the human brain," Nature, Nature, vol. 435(7045), pages 1102-1107, June.
    • Handle: RePEc:nat:nature:v:435:y:2005:i:7045:d:10.1038_nature03687
    DOI: 10.1038/nature03687
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    Cited by:

    1. Rodrigo Quian Quiroga & Marta Boscaglia & Jacques Jonas & Hernan G. Rey & Xiaoqian Yan & Louis Maillard & Sophie Colnat-Coulbois & Laurent Koessler & Bruno Rossion, 2023. "Single neuron responses underlying face recognition in the human midfusiform face-selective cortex," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    2. Luca D. Kolibius & Frederic Roux & George Parish & Marije Wal & Mircea Plas & Ramesh Chelvarajah & Vijay Sawlani & David T. Rollings & Johannes D. Lang & Stephanie Gollwitzer & Katrin Walther & Rüdige, 2023. "Hippocampal neurons code individual episodic memories in humans," Nature Human Behaviour, Nature, vol. 7(11), pages 1968-1979, November.
    3. Ahalya Prabhakar & Todd Murphey, 2022. "Mechanical intelligence for learning embodied sensor-object relationships," Nature Communications, Nature, vol. 13(1), pages 1-8, December.
    4. Thomas P. Reber & Sina Mackay & Marcel Bausch & Marcel S. Kehl & Valeri Borger & Rainer Surges & Florian Mormann, 2023. "Single-neuron mechanisms of neural adaptation in the human temporal lobe," Nature Communications, Nature, vol. 14(1), pages 1-9, December.
    5. Umut Güçlü & Marcel A J van Gerven, 2014. "Unsupervised Feature Learning Improves Prediction of Human Brain Activity in Response to Natural Images," PLOS Computational Biology, Public Library of Science, vol. 10(8), pages 1-12, August.
    6. Shiva Farashahi & Alireza Soltani, 2021. "Computational mechanisms of distributed value representations and mixed learning strategies," Nature Communications, Nature, vol. 12(1), pages 1-18, December.
    7. Nanyi Fei & Zhiwu Lu & Yizhao Gao & Guoxing Yang & Yuqi Huo & Jingyuan Wen & Haoyu Lu & Ruihua Song & Xin Gao & Tao Xiang & Hao Sun & Ji-Rong Wen, 2022. "Towards artificial general intelligence via a multimodal foundation model," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    8. Henning Sprekeler & Christian Michaelis & Laurenz Wiskott, 2007. "Slowness: An Objective for Spike-Timing–Dependent Plasticity?," PLOS Computational Biology, Public Library of Science, vol. 3(6), pages 1-13, June.
    9. Louis Kang & Taro Toyoizumi, 2024. "Distinguishing examples while building concepts in hippocampal and artificial networks," Nature Communications, Nature, vol. 15(1), pages 1-19, December.
    10. Jörn Diedrichsen & Nikolaus Kriegeskorte, 2017. "Representational models: A common framework for understanding encoding, pattern-component, and representational-similarity analysis," PLOS Computational Biology, Public Library of Science, vol. 13(4), pages 1-33, April.
    11. Chiara Gastaldi & Tilo Schwalger & Emanuela De Falco & Rodrigo Quian Quiroga & Wulfram Gerstner, 2021. "When shared concept cells support associations: Theory of overlapping memory engrams," PLOS Computational Biology, Public Library of Science, vol. 17(12), pages 1-44, December.
    12. Carlo Baldassi & Alireza Alemi-Neissi & Marino Pagan & James J DiCarlo & Riccardo Zecchina & Davide Zoccolan, 2013. "Shape Similarity, Better than Semantic Membership, Accounts for the Structure of Visual Object Representations in a Population of Monkey Inferotemporal Neurons," PLOS Computational Biology, Public Library of Science, vol. 9(8), pages 1-20, August.
    13. Dock H. Duncan & Dirk Moorselaar & Jan Theeuwes, 2023. "Pinging the brain to reveal the hidden attentional priority map using encephalography," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    14. David Balduzzi & Giulio Tononi, 2009. "Qualia: The Geometry of Integrated Information," PLOS Computational Biology, Public Library of Science, vol. 5(8), pages 1-24, August.
    15. Jongwoon Kim & Hengji Huang & Earl T. Gilbert & Kaiser C. Arndt & Daniel Fine English & Xiaoting Jia, 2024. "T-DOpE probes reveal sensitivity of hippocampal oscillations to cannabinoids in behaving mice," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    16. Martinez-Saito, Mario, 2022. "Discrete scaling and criticality in a chain of adaptive excitable integrators," Chaos, Solitons & Fractals, Elsevier, vol. 163(C).

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