@article {4563,
	title = {A human single-neuron dataset for object recognition.},
	journal = {Sci Data},
	volume = {12},
	year = {2025},
	month = {2025 Jan 15},
	pages = {79},
	abstract = {<p>Object recognition is fundamental to how we interact with and interpret the world around us. The human amygdala and hippocampus play a key role in object recognition, contributing to both the encoding and retrieval of visual information. Here, we recorded single-neuron activity from the human amygdala and hippocampus when neurosurgical epilepsy patients performed a one-back task using naturalistic object stimuli. We employed two sets of naturalistic object images from leading datasets extensively used in primate neural recordings and computer vision models: we recorded 1204 neurons using the ImageNet stimuli, which included broader object categories (10 different images per category for 50 categories), and we recorded 512 neurons using the Microsoft COCO stimuli, which featured a higher number of images per category (50 different images per category for 10 categories). Together, our extensive dataset, offering the highest spatial and temporal resolution currently available in humans, will not only facilitate a comprehensive analysis of the neural correlates of object recognition but also provide valuable opportunities for training and validating computational models.</p>},
	keywords = {Amygdala, Epilepsy, Hippocampus, Humans, Neurons, Pattern Recognition, Visual, Recognition, Psychology},
	issn = {2052-4463},
	doi = {10.1038/s41597-024-04265-1},
	author = {Cao, Runnan and Brunner, Peter and Brandmeir, Nicholas J and Willie, Jon T and Wang, Shuo}
}
@article {4556,
	title = {Neural mechanisms of face familiarity and learning in the human amygdala and hippocampus},
	journal = {Cell reports},
	volume = {43},
	year = {2024},
	author = {Cao, Runnan and Wang, Jinge and Brunner, Peter and Willie, Jon T and Li, Xin and Rutishauser, Ueli and Brandmeir, Nicholas J and Wang, Shuo}
}
@article {4585,
	title = {Neural mechanisms of face familiarity and learning in the human amygdala and hippocampus.},
	journal = {Cell Rep},
	volume = {43},
	year = {2024},
	month = {2024 Jan 23},
	pages = {113520},
	abstract = {<p>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.</p>},
	keywords = {Amygdala, Facial Recognition, Hippocampus, Humans, Learning, Pattern Recognition, Visual, Recognition, Psychology},
	issn = {2211-1247},
	doi = {10.1016/j.celrep.2023.113520},
	author = {Cao, Runnan and Wang, Jinge and Brunner, Peter and Willie, Jon T and Li, Xin and Rutishauser, Ueli and Brandmeir, Nicholas J and Wang, Shuo}
}
