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Computational single-neuron mechanisms of visual object coding in the human temporal lobe
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  • Published: 01 February 2026

Computational single-neuron mechanisms of visual object coding in the human temporal lobe

  • Runnan Cao  ORCID: orcid.org/0000-0001-5827-99031,
  • Jie Zhang  ORCID: orcid.org/0000-0002-1464-86391,
  • Jie Zheng  ORCID: orcid.org/0000-0002-9086-37602,
  • Yue Wang1,
  • Peter Brunner  ORCID: orcid.org/0000-0002-2588-27543,
  • Jon T. Willie  ORCID: orcid.org/0000-0001-9565-43383,4 &
  • …
  • Shuo Wang  ORCID: orcid.org/0000-0003-2562-02251,3 

Nature Communications , Article number:  (2026) Cite this article

We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.

Subjects

  • Cognitive neuroscience
  • Visual system

Abstract

Understanding how the human brain encodes visual objects involves deciphering the neural computations and circuits in the temporal lobe. Here, we recorded intracranial EEG from the human ventral temporal cortex (VTC) and medial temporal lobe (MTL), as well as single-neuron activity in the MTL, to investigate the computational mechanisms of neural object coding. The VTC exhibited axis-based feature coding, and a neural feature space could be constructed using VTC neural axes, within which visual objects clustered according to high-level categorical relationships. Importantly, MTL neurons encoded receptive fields within this VTC neural feature space, exhibiting selective responses to objects that shared perceptual and conceptual similarities. This computational framework, therefore, explains how dense, feature-based representations in the VTC are transformed into sparse, high-level representations in the MTL. We further validated our findings using an additional dataset with different stimuli. Notably, we uncovered the physiological basis of this computational framework by demonstrating VTC-MTL interactions at multiple levels. Together, our neural computational framework provides a mechanistic understanding of the neural processes underlying object recognition.

Data availability

All data that support the findings of this study are publicly available on OSF (https://osf.io/x9u84/). Source data are provided with this paper.

Code availability

The source code for this study is publicly available on OSF (https://osf.io/x9u84/).

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Acknowledgements

We thank all patients for their participation. This research was supported by the NIH (K99EY036650 [R.C.], R01MH129426 [S.W.], R01MH120194 [J.T.W.], R01EB026439 [P.B.], U24NS109103 [P.B.], U01NS108916 [P.B.], U01NS128612 [P.B.], R21NS128307 [P.B.], P41EB018783 [P.B.]), AFOSR (FA9550-21-1-0088 [S.W.]), NSF (BCS-1945230 [S.W.]), Brain & Behavior Research Foundation (33261 [R.C.]), and McDonnell Center for Systems Neuroscience ([R.C.]). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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Authors and Affiliations

  1. Department of Radiology, Washington University in St. Louis, St. Louis, MO, USA

    Runnan Cao, Jie Zhang, Yue Wang & Shuo Wang

  2. Department of Biomedical Engineering, University of California Davis, Davis, CA, USA

    Jie Zheng

  3. Department of Neurosurgery, Washington University in St. Louis, St. Louis, MO, USA

    Peter Brunner, Jon T. Willie & Shuo Wang

  4. Department of Neurosurgery, University of Texas at Austin, Austin, TX, USA

    Jon T. Willie

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Contributions

R.C. and S.W. designed the research. R.C., J.Zhang, Y.W., P.B., and S.W. performed experiments. J.T.W. performed surgery. R.C., J.Zhang, J.Zheng, and S.W. analyzed data. R.C., J.T.W., and S.W. wrote the paper. All authors discussed the results and contributed toward the manuscript.

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Correspondence to Runnan Cao or Shuo Wang.

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Cao, R., Zhang, J., Zheng, J. et al. Computational single-neuron mechanisms of visual object coding in the human temporal lobe. Nat Commun (2026). https://doi.org/10.1038/s41467-026-68954-8

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  • Received: 10 December 2024

  • Accepted: 16 January 2026

  • Published: 01 February 2026

  • DOI: https://doi.org/10.1038/s41467-026-68954-8

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