Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Feature-based encoding of face identity by single neurons in the human amygdala and hippocampus

Nature Human Behaviour volume 9, pages 1959–1974 (2025)Cite this article

Subjects

Abstract

Neurons in the human amygdala and hippocampus are classically thought to encode a person’s identity invariant to visual features. However, it remains largely unknown how visual information from higher visual cortical areas is translated into such a semantic representation of an individual person. Here, across four experiments (3,581 neurons from 19 neurosurgical patients over 111 sessions), we demonstrate a region-based feature code for faces, where neurons encode faces on the basis of shared visual features rather than associations of known concepts, contrary to prevailing views. Feature neurons encode groups of faces regardless of their identity, broad semantic categories or familiarity; and the coding regions (that is, receptive fields) predict feature neurons’ response to new face stimuli. Together, our results reveal a new class of neurons that bridge perception-driven representation of facial features with mnemonic semantic representations, which may form the basis for declarative memory.

Access through your institution
Buy or subscribe

This is a preview of subscription content, access via your institution

Access options

Access through your institution

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Feature-based neuronal coding of face identities.
Fig. 2: Population summary of region-based feature coding in identity neurons and comparison between visual similarity and conceptual association.
Fig. 3: Characterization of feature neurons.
Fig. 4: Validation and generalization of region-based feature coding with unfamiliar and model faces.

Similar content being viewed by others

Data availability

All data supporting the findings of this study, including the CelebA dataset, FBI dataset, FaceGen dataset and monkey dataset, are publicly available on OSF at https://doi.org/10.17605/OSF.IO/36KZC (ref. 63). We also analysed identity neurons from a publicly available human single-neuron dataset (https://doi.org/10.25392/leicester.data.8796335.v1).

Code availability

The source code for this study is publicly available on OSF at https://doi.org/10.17605/OSF.IO/36KZC (ref. 63).

References

  1. Freeman, W. J. Mass Action in The Nervous System (Academic, 1975).

  2. Hinton, G. E., McClelland, J. L. & Rumelhart, D. E. in Parallel Distributed Processing: Explorations in the Microstructure of Cognition Vol. 1 (eds Rumelhart, D. E. & McClelland, J. L.) 77–109 (MIT Press, 1986).

  3. Rolls, E. T., Treves, A. & Tovee, M. J. The representational capacity of the distributed encoding of information provided by populations of neurons in primate temporal visual cortex. Exp. Brain Res. 114, 149–162 (1997).

    Article  PubMed  CAS  Google Scholar 

  4. Churchland, P. S. & Sejnowski, T. J. The Computational Brain (MIT Press, 2016).

  5. Turk, M. A. & Pentland, A. P. Face recognition using eigenfaces. In Proc. 1991 IEEE Computer Society Conference on Computer Vision and Pattern Recognition 586–591 (IEEE, 1991).

  6. Freiwald, W. A., Tsao, D. Y. & Livingstone, M. S. A face feature space in the macaque temporal lobe. Nat. Neurosci. 12, 1187–1196 (2009).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  7. Chang, L. & Tsao, D. Y. The code for facial identity in the primate brain. Cell 169, 1013–1028.e14 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  8. Bashivan, P., Kar, K. & DiCarlo, J. J. Neural population control via deep image synthesis. Science 364, eaav9436 (2019).

    Article  PubMed  CAS  Google Scholar 

  9. Ponce, C. R. et al. Evolving images for visual neurons using a deep generative network reveals coding principles and neuronal preferences. Cell 177, 999–1009.e10 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  10. Bao, P. et al. A map of object space in primate inferotemporal cortex. Nature 583, 103–108 (2020).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  11. Loffler, G. et al. fMRI evidence for the neural representation of faces. Nat. Neurosci. 8, 1386–1391 (2005).

    Article  PubMed  CAS  Google Scholar 

  12. Carlin, J. D. & Kriegeskorte, N. Adjudicating between face-coding models with individual-face fMRI responses. PLoS Comput. Biol. 13, e1005604 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  13. Cao, R. et al. A flexible neural representation of faces in the human brain. Cereb. Cortex Commun. 1, tgaa055 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  14. Barlow, H. B. Single units and sensation: a neuron doctrine for perceptual psychology? Perception 1, 371–394 (1972).

    Article  PubMed  CAS  Google Scholar 

  15. Valentine, T. A unified account of the effects of distinctiveness, inversion, and race in face recognition. Q. J. Exp. Psychol. A 43, 161–204 (1991).

    Article  PubMed  CAS  Google Scholar 

  16. Quian Quiroga, R. et al. Invariant visual representation by single neurons in the human brain. Nature 435, 1102–1107 (2005).

    Article  PubMed  Google Scholar 

  17. Quian Quiroga, R. Concept cells: the building blocks of declarative memory functions. Nat. Rev. Neurosci. 13, 587–597 (2012).

    Article  Google Scholar 

  18. De Falco, E. et al. Long-term coding of personal and universal associations underlying the memory web in the human brain. Nat. Commun. 7, 13408 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  19. Rey, H. G. et al. Encoding of long-term associations through neural unitization in the human medial temporal lobe. Nat. Commun. 9, 4372 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  20. Bausch, M. et al. Concept neurons in the human medial temporal lobe flexibly represent abstract relations between concepts. Nat. Commun. 12, 6164 (2021).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  21. Rey, H. G. et al. Single neuron coding of identity in the human hippocampal formation. Curr. Biol. 30, 1152–1159.e3 (2020).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  22. Tyree, T. J., Metke, M. & Miller, C. T. Cross-modal representation of identity in the primate hippocampus. Science 382, 417–423 (2023).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  23. Reber, T. P. et al. Representation of abstract semantic knowledge in populations of human single neurons in the medial temporal lobe. PLoS Biol. 17, e3000290 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  24. Lin, C., Keles, U. & Adolphs, R. Four dimensions characterize attributions from faces using a representative set of English trait words. Nat. Commun. 12, 5168 (2021).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  25. Leopold, D. A., Bondar, I. V. & Giese, M. A. Norm-based face encoding by single neurons in the monkey inferotemporal cortex. Nature 442, 572–575 (2006).

    Article  PubMed  CAS  Google Scholar 

  26. Oosterhof, N. N. & Todorov, A. The functional basis of face evaluation. Proc. Natl Acad. Sci. USA 105, 11087–11092 (2008).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  27. Rutishauser, U. Testing models of human declarative memory at the single-neuron level. Trends Cogn. Sci. 23, 510–524 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  28. Cao, R. et al. A neuronal code for object representation and memory in the human amygdala and hippocampus. Nat. Commun. 16, 1510 (2025).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  29. Cao, R. et al. Neural mechanisms of face familiarity and learning in the human amygdala and hippocampus. Cell Rep. 43, 113520 (2024).

    Article  PubMed  CAS  Google Scholar 

  30. Kreiman, G., Koch, C. & Fried, I. Category-specific visual responses of single neurons in the human medial temporal lobe. Nat. Neurosci. 3, 946–953 (2000).

    Article  PubMed  CAS  Google Scholar 

  31. Fried, I., MacDonald, K. A. & Wilson, C. L. Single neuron activity in human hippocampus and amygdala during recognition of faces and objects. Neuron 18, 753–765 (1997).

    Article  PubMed  CAS  Google Scholar 

  32. Wang, S. et al. Neurons in the human amygdala selective for perceived emotion. Proc. Natl Acad. Sci. USA 111, E3110–E3119 (2014).

    PubMed  PubMed Central  CAS  Google Scholar 

  33. O’keefe, J. & Nadel, L. The Hippocampus as a Cognitive Map (Oxford Univ. Press, 1978).

  34. Behrens, T. E. J. et al. What is a cognitive map? Organizing knowledge for flexible behavior. Neuron 100, 490–509 (2018).

    Article  PubMed  CAS  Google Scholar 

  35. Roweis, S. T. & Saul, L. K. Nonlinear dimensionality reduction by locally linear embedding. Science 290, 2323–2326 (2000).

    Article  PubMed  CAS  Google Scholar 

  36. Tenenbaum, J. B., Silva, V. D. & Langford, J. C. A global geometric framework for nonlinear dimensionality reduction. Science 290, 2319–2323 (2000).

    Article  PubMed  CAS  Google Scholar 

  37. Wang, S. et al. The human amygdala parametrically encodes the intensity of specific facial emotions and their categorical ambiguity. Nat. Commun. 8, 14821 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  38. Wang, S. et al. Encoding of target detection during visual search by single neurons in the human brain. Curr. Biol. 28, 2058–2069.e4 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  39. Cao, R. et al. Encoding of facial features by single neurons in the human amygdala and hippocampus. Commun. Biol. 4, 1394 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  40. Cao, R. et al. A neuronal social trait space for first impressions in the human amygdala and hippocampus. Mol. Psychiatry 27, 3501–3509 (2022).

    Article  PubMed  Google Scholar 

  41. Liu, Z. et al. Deep learning face attributes in the wild. In Proc. International Conference on Computer Vision (ICCV) 3730–3738 (IEEE, 2015).

  42. Parsons, S., Kruijt, A.-W. & Fox, E. Psychological science needs a standard practice of reporting the reliability of cognitive-behavioral measurements. Adv. Methods Pract. Psychol. Sci. 2, 378–395 (2019).

    Article  Google Scholar 

  43. Grossman, S. et al. Convergent evolution of face spaces across human face-selective neuronal groups and deep convolutional networks. Nat. Commun. 10, 4934 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  44. Brainard, D. H. The psychophysics toolbox. Spat. Vis. 10, 433–436 (1997).

    Article  PubMed  CAS  Google Scholar 

  45. Parkhi, O. M., Vedaldi, A. & Zisserman, A. Deep Face Recognition. In BMVC 2015—Proceedings of the British Machine Vision Conference 2015 (British Machine Vision Association, 2015).

  46. Hinton, G. E. & Roweis, S. T. Stochastic neighbor embedding. Adv. Neural Inf. Process. Syst. 15, 857–864 (2002).

  47. van der Maaten, L. & Hinton, G. Visualizing data using t-SNE. J. Mach. Learn. Res. 9, 2579–2605 (2008).

    Google Scholar 

  48. Rutishauser, U., Mamelak, A. N. & Schuman, E. M. Single-trial learning of novel stimuli by individual neurons of the human hippocampus-amygdala complex. Neuron 49, 805–813 (2006).

    Article  PubMed  CAS  Google Scholar 

  49. Rutishauser, U. et al. Human memory strength is predicted by theta-frequency phase-locking of single neurons. Nature 464, 903–907 (2010).

    Article  PubMed  CAS  Google Scholar 

  50. Rutishauser, U., Schuman, E. M. & Mamelak, A. N. Online detection and sorting of extracellularly recorded action potentials in human medial temporal lobe recordings, in vivo. J. Neurosci. Methods 154, 204–224 (2006).

    Article  PubMed  Google Scholar 

  51. Fried, I. et al. Single Neuron Studies of the Human Brain: Probing Cognition (MIT Press, 2014).

  52. Kar, K. et al. Evidence that recurrent circuits are critical to the ventral stream’s execution of core object recognition behavior. Nat. Neurosci. 22, 974–983 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  53. Kar, K. & DiCarlo, J. J. Fast recurrent processing via ventrolateral prefrontal cortex is needed by the primate ventral stream for robust core visual object recognition. Neuron 109, 164–176.e5 (2021).

    Article  PubMed  CAS  Google Scholar 

  54. Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. B Stat. Methodol. 57, 289–300 (1995).

    Article  Google Scholar 

  55. Rainer, G., Asaad, W. F. & Miller, E. K. Selective representation of relevant information by neurons in the primate prefrontal cortex. Nature 393, 577–579 (1998).

    Article  PubMed  CAS  Google Scholar 

  56. Minxha, J. et al. Fixations gate species-specific responses to free viewing of faces in the human and macaque amygdala. Cell Rep. 18, 878–891 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  57. Meyers, E. The neural decoding toolbox. Front. Neuroinform. 7, 8 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  58. Rutishauser, U. et al. Representation of retrieval confidence by single neurons in the human medial temporal lobe. Nat. Neurosci. 18, 1041–1050 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  59. Wang, S. et al. Abstract goal representation in visual search by neurons in the human pre-supplementary motor area. Brain 142, 3530–3549 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  60. Yamins, D. L. K. et al. Performance-optimized hierarchical models predict neural responses in higher visual cortex. Proc. Natl Acad. Sci. USA 111, 8619–8624 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  61. Kriegeskorte, N., Mur, M. & Bandettini, P. Representational similarity analysis – connecting the branches of systems neuroscience. Front. Syst. Neurosci. 2, 4 (2008).

    PubMed  PubMed Central  Google Scholar 

  62. Mormann, F. et al. Latency and selectivity of single neurons indicate hierarchical processing in the human medial temporal lobe. J. Neurosci. 28, 8865–8872 (2008).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  63. Cao, R. Data for “Feature-based encoding of face identity by single neurons in the human amygdala and hippocampus”. OSF https://doi.org/10.17605/OSF.IO/36KZC (2025).

Download references

Acknowledgements

We thank all patients for their participation; staff from WVU Ruby Memorial Hospital for support with patient testing; M. Yin and S. Uddenberg for help with analysis; J. Dawson for contributing the FBI Twins dataset; and R. Adolphs, M. Raichle, C. Ponce, L. Chang, D. Tsao, L. She and P. Webster for discussion and valuable comments. This research was supported by the AFOSR (FA9550-21-1-0088, S.W.), NSF (BCS-1945230, S.W. and X.L.; IIS-2114644, X.L. and S.W.), NIH (K99EY036650, R.C.; R01MH129426, S.W. and X.L.; R01MH120194, J.T.W.; R01EB026439, P.B.; U24NS109103, P.B.; U01NS108916, P.B.; U01NS128612, P.B.; R21NS128307, P.B.; P41EB018783, P.B.), the McDonnell Center for Systems Neuroscience (R.C.), Fondazione Neurone (P.B.), and the Dana Foundation (S.W.). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Author information

Author notes

  1. These authors contributed equally: Jinge Wang, Chujun Lin, Emanuela De Falco, Alina Peter.

Authors and Affiliations

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

    Runnan Cao & Shuo Wang

  2. Lane Department of Computer Science and Electrical Engineering, West Virginia University, Morgantown, WV, USA

    Jinge Wang

  3. Department of Psychology, University of California San Diego, La Jolla, CA, USA

    Chujun Lin

  4. Laboratory of Cognitive Neuroscience, École Polytechnique Fédérale de Lausanne, Route Cantonale, Lausanne, Switzerland

    Emanuela De Falco

  5. Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA

    Alina Peter & James J. DiCarlo

  6. Department of Neurosurgery, Medical College of Wisconsin, Milwaukee, WI, USA

    Hernan G. Rey

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

    Peter Brunner, Jon T. Willie & Shuo Wang

  8. Booth School of Business, University of Chicago, Chicago, IL, USA

    Alexander Todorov

  9. Departments of Neurosurgery and Neurology, Cedars-Sinai Medical Center, Los Angeles, CA, USA

    Ueli Rutishauser

  10. Department of Computer Science, University at Albany, Albany, NY, USA

    Xin Li

  11. Department of Neurosurgery, West Virginia University, Morgantown, WV, USA

    Nicholas J. Brandmeir

Authors
  1. Runnan Cao
    View author publications

    Search author on:PubMed Google Scholar

  2. Jinge Wang
    View author publications

    Search author on:PubMed Google Scholar

  3. Chujun Lin
    View author publications

    Search author on:PubMed Google Scholar

  4. Emanuela De Falco
    View author publications

    Search author on:PubMed Google Scholar

  5. Alina Peter
    View author publications

    Search author on:PubMed Google Scholar

  6. Hernan G. Rey
    View author publications

    Search author on:PubMed Google Scholar

  7. Peter Brunner
    View author publications

    Search author on:PubMed Google Scholar

  8. Jon T. Willie
    View author publications

    Search author on:PubMed Google Scholar

  9. James J. DiCarlo
    View author publications

    Search author on:PubMed Google Scholar

  10. Alexander Todorov
    View author publications

    Search author on:PubMed Google Scholar

  11. Ueli Rutishauser
    View author publications

    Search author on:PubMed Google Scholar

  12. Xin Li
    View author publications

    Search author on:PubMed Google Scholar

  13. Nicholas J. Brandmeir
    View author publications

    Search author on:PubMed Google Scholar

  14. Shuo Wang
    View author publications

    Search author on:PubMed Google Scholar

Contributions

R.C., A.T., X.L. and S.W. designed the research. R.C., A.P., P.B. and S.W. performed experiments. N.J.B. and J.T.W. performed surgery. R.C., J.W., C.L., E.D.F., A.P., H.G.R., X.L. and S.W. analysed data. R.C, J.J.D., A.T., U.R., X.L. and S.W. wrote the paper. All authors discussed the results and contributed to the paper.

Corresponding authors

Correspondence to Runnan Cao or Shuo Wang.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Human Behaviour thanks the anonymous reviewers for their contribution to the peer review of this work. Peer reviewer reports are available.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary Results, Discussion, Tables 1 and 2, and Figs. 1–16.

Reporting Summary

Peer Review File

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Cao, R., Wang, J., Lin, C. et al. Feature-based encoding of face identity by single neurons in the human amygdala and hippocampus. Nat Hum Behav 9, 1959–1974 (2025). https://doi.org/10.1038/s41562-025-02218-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue date:

  • DOI: https://doi.org/10.1038/s41562-025-02218-1

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing