A human single-neuron dataset for object recognition
Abstract 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 hum...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-024-04265-1 |
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| _version_ | 1832595015905312768 |
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| author | Runnan Cao Peter Brunner Nicholas J. Brandmeir Jon T. Willie Shuo Wang |
| author_facet | Runnan Cao Peter Brunner Nicholas J. Brandmeir Jon T. Willie Shuo Wang |
| author_sort | Runnan Cao |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-f8d921ff9dc44f8dab158ee1484ac673 |
| institution | Kabale University |
| issn | 2052-4463 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Data |
| spelling | doaj-art-f8d921ff9dc44f8dab158ee1484ac6732025-01-19T12:09:45ZengNature PortfolioScientific Data2052-44632025-01-0112111110.1038/s41597-024-04265-1A human single-neuron dataset for object recognitionRunnan Cao0Peter Brunner1Nicholas J. Brandmeir2Jon T. Willie3Shuo Wang4Department of Radiology, Washington University in St. LouisDepartment of Neurosurgery, Washington University in St. LouisDepartment of Neurosurgery, West Virginia UniversityDepartment of Neurosurgery, Washington University in St. LouisDepartment of Radiology, Washington University in St. LouisAbstract 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.https://doi.org/10.1038/s41597-024-04265-1 |
| spellingShingle | Runnan Cao Peter Brunner Nicholas J. Brandmeir Jon T. Willie Shuo Wang A human single-neuron dataset for object recognition Scientific Data |
| title | A human single-neuron dataset for object recognition |
| title_full | A human single-neuron dataset for object recognition |
| title_fullStr | A human single-neuron dataset for object recognition |
| title_full_unstemmed | A human single-neuron dataset for object recognition |
| title_short | A human single-neuron dataset for object recognition |
| title_sort | human single neuron dataset for object recognition |
| url | https://doi.org/10.1038/s41597-024-04265-1 |
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