Feature-based encoding of face identity by single neurons in the human medial temporal lobe

Neurons in the human medial temporal lobe (MTL) that are selective for the identity of specific people are classically thought to encode identity invariant to visual features. However, it remains largely unknown how visual information from higher visual cortex is translated into a semantic representation of an individual person. Here, we show that some MTL neurons are selective to multiple different face identities on the basis of shared features that form clusters in the representation of a deep neural network trained to recognize faces. Contrary to prevailing views, we find that these neurons represent an individual’s face with feature-based encoding, rather than through association with concepts. The response of feature neurons did not depend on face identity nor face familiarity, and the region of feature space to which they are tuned predicted their response to new face stimuli. Our results provide critical evidence bridging the perception-driven representation of facial features in the higher visual cortex and the memory-driven representation of semantics in the MTL, which may form the basis for declarative memory