Cao Runnan, Brunner Peter, Brandmeir Nicholas J, Willie Jon T, Wang Shuo
Department of Radiology, Washington University in St. Louis, St. Louis, MO, 63110, USA.
Department of Neurosurgery, Washington University in St. Louis, St. Louis, MO, 63110, USA.
Sci Data. 2025 Jan 15;12(1):79. doi: 10.1038/s41597-024-04265-1.
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.
物体识别是我们与周围世界互动并对其进行解读的基础。人类杏仁核和海马体在物体识别中起着关键作用，对视觉信息的编码和检索都有贡献。在此，当神经外科癫痫患者使用自然物体刺激执行一项“单后”任务时，我们记录了人类杏仁核和海马体的单神经元活动。我们使用了两组在灵长类神经记录和计算机视觉模型中广泛使用的领先数据集中的自然物体图像：我们使用ImageNet刺激记录了1204个神经元，其中包括更广泛的物体类别（50个类别，每个类别有10张不同图像），并且我们使用Microsoft COCO刺激记录了512个神经元，其特点是每个类别有更多图像（10个类别，每个类别有50张不同图像）。我们的这个广泛数据集提供了目前人类可获得的最高空间和时间分辨率，不仅将有助于对物体识别的神经关联进行全面分析，还将为训练和验证计算模型提供宝贵机会。
