A human single-neuron dataset for face perception

The human amygdala and hippocampus have long been associated with face perception. Here, we present a dataset of single-neuron activity in the human amygdala and hippocampus during face perception. We recorded 2082 neurons from the human amygdala and hippocampus when neurosurgical patients with intractable epilepsy performed a one-back task using natural face stimuli, which mimics natural face perception. Specifically, our data include (1) single-neuron activity from the amygdala (996 neurons) and hippocampus (1086 neurons), (2) eye movements (gaze position and pupil), (3) psychological assessment of the patients, and (4) social trait judgment ratings from a subset of patients and a large sample of participants from the general population. Together, our comprehensive dataset with a large population of neurons can facilitate multifaceted investigation of face perception with the highest spatial and temporal resolution currently available in humans

Face identity coding in the deep neural network and primate brain

A central challenge in face perception research is to understand how neurons encode face identities. This challenge has not been met largely due to the lack of simultaneous access to the entire face processing neural network and the lack of a comprehensive multifaceted model capable of characterizing a large number of facial features. Here, we addressed this challenge by conducting in silico experiments using a pre-trained face recognition deep neural network (DNN) with a diverse array of stimuli. We identified a subset of DNN units selective to face identities, and these identity-selective units demonstrated generalized discriminability to novel faces. Visualization and manipulation of the network revealed the importance of identity-selective units in face recognition. Importantly, using our monkey and human single-neuron recordings, we directly compared the response of artificial units with real primate neurons to the same stimuli and found that artificial units shared a similar representation of facial features as primate neurons. We also observed a region-based feature coding mechanism in DNN units as in human neurons. Together, by directly linking between artificial and primate neural systems, our results shed light on how the primate brain performs face recognition tasks