发表机构
Neuroscience Interdepartmental Program, Stanford University; Department of Neurobiology, Stanford University; Howard Hughes Medical Institute, Stanford University（斯坦福大学神经科学跨学科项目; 斯坦福大学神经生物学系; 斯坦福大学霍华德·霍夫曼医学研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对灵长类动物视觉系统背侧流建模缺乏大规模数据集的问题，提出STSBench数据集，它包含超2000个神经元记录，比现有数据集增加近50倍，可用于背侧流神经元反应编码模型基准测试及视觉输入重建。
AI 中文摘要
灵长类动物视觉系统通常分为两个流——负责物体识别的腹侧流和负责编码空间关系与运动的背侧流。近期研究表明，在物体识别任务上预训练的卷积神经网络能有效预测腹侧流中的神经元反应。但由于缺乏涵盖背侧流区域的大规模数据集，背侧流的类似模型仍未充分发展。为填补这一空白，我们展示了STSBench，这是一个来自颞上沟（STS）超过2000个神经元的大规模单神经元记录数据集，比现有背侧流数据集增加了近50倍，是在恒河猴观看数千个独特自然视频时收集的。我们表明，我们的数据集可用于对背侧流神经元反应的编码模型进行基准测试，并从神经活动中重建视觉输入。
英文摘要
The primate visual system is typically divided into two streams - the ventral stream, responsible for object recognition, and the dorsal stream, responsible for encoding spatial relations and motion. Recent studies have shown that convolutional neural networks (CNNs) pretrained on object recognition tasks are remarkably effective at predicting neuronal responses in the ventral stream, shedding light on the neural mechanisms underlying object recognition. However, similar models of the dorsal stream remain underdeveloped due to the lack of large scale datasets encompassing dorsal stream areas. To address this gap, we present STSBench, a dataset of large-scale, single neuron recordings from over 2,000 neurons in the superior temporal sulcus (STS), a nearly 50-fold increase over existing dorsal stream datasets, collected while Rhesus macaques viewed thousands of unique, natural videos. We show that our dataset can be used for benchmarking encoding models of dorsal stream neuronal responses and reconstructing visual input from neural activity.
Comments21 pages, 10 figures, Advances in Neural Information Processing Systems 38 (NeurIPS 2025) Datasets and Benchmarks Track
Journal refAdvances in Neural Information Processing Systems 38 (2025)
