构建了首个大规模灵长类背侧流视觉皮层神经活动数据集
STSBench: A Large-Scale Dataset for Modeling Neuronal Activity in the Dorsal Stream of Primate Visual Cortex
- 采集2000+神经元在自然视频刺激下的放电数据
- 数据量达现有背侧流数据的50倍，支持模型训练与验证
- 适用于视觉重建与神经编码模型评估
灵长类视觉系统通常分为腹侧流（负责物体识别）和背侧流（负责空间关系与运动编码）。近年研究发现，基于物体识别预训练的卷积神经网络（CNN）能有效预测腹侧流神经元响应，揭示了物体识别的神经机制。但背侧流缺乏大规模数据支持，相关模型发展滞后。为此，我们提出STSBench，一个包含超过2,000个神经元在上颞沟（STS）区域的大型单神经元记录数据集，覆盖数千个独特自然视频，相较现有背侧流数据集扩大近50倍。该数据集可用于背侧流神经响应的编码模型基准测试，以及从神经活动中重构视觉输入。
原文摘要 · Abstract (English)
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.
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