Neural mechanisms of face familiarity and learning in the human amygdala and hippocampus

Runnan Cao, Jinge Wang, Peter Brunner, Jon T. Willie, Xin Li, Ueli Rutishauser, Nicholas J. Brandmeir, Shuo Wang

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Recognizing familiar faces and learning new faces play an important role in social cognition. However, the underlying neural computational mechanisms remain unclear. Here, we record from single neurons in the human amygdala and hippocampus and find a greater neuronal representational distance between pairs of familiar faces than unfamiliar faces, suggesting that neural representations for familiar faces are more distinct. Representational distance increases with exposures to the same identity, suggesting that neural face representations are sharpened with learning and familiarization. Furthermore, representational distance is positively correlated with visual dissimilarity between faces, and exposure to visually similar faces increases representational distance, thus sharpening neural representations. Finally, we construct a computational model that demonstrates an increase in the representational distance of artificial units with training. Together, our results suggest that the neuronal population geometry, quantified by the representational distance, encodes face familiarity, similarity, and learning, forming the basis of face recognition and memory.

Original languageEnglish
Article number113520
JournalCell Reports
Volume43
Issue number1
DOIs
StatePublished - Jan 23 2024

Keywords

  • CP: Neuroscience
  • amygdala
  • face
  • familiarity
  • hippocampus
  • human single-neuron recordings
  • learning
  • representational distance
  • similarity

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