muton (2022-07-28 11:58):
#paper DOI: 10.1371/journal.pcbi.1009267 Unveiling functions of the visual cortex using task-specific deep neural networks.人类的视觉感知是一种复杂的认知能力,它是由大脑不同皮层区域控制调节的。然而目前这些区域的确切功能我们了解的仍不完全清楚,进而这些区域是如何协调视觉感知的也没有确切的答案。目前的观点认为视觉信息的转变过程是通过不同功能区域的层次化计算,通常我们概括为这些功能区域为腹侧和背侧视觉通路。无论是发现各个视觉皮层区域的确切功能还是利用计算建模的方法实现这种功能都是具有挑战性的,但也是我们的最终诉求。深度神经网络(DNNs)用于实现建模和预测视觉区域反应的一种较有前景的方法。本文通过比较不同视觉任务中的fMRI数据集与针对不同视觉任务优化过的DNN 模型子集的相关(作者选择了通过Taskonomy数据集训练的18个DNNs模型,这些模型分别对应于室内场景图片理解的18个不同任务的优化)发现了视觉信息沿腹侧和背侧视觉通路的结构化映射。低级视觉任务映射到早期视觉皮层,三维场景感知任务映射到背侧流,语义任务映射到腹侧流。文章的亮点可能就是通过模型和人脑实际数据相似性比较的方法能够得出哪些脑区贡献于哪些任务的这种思路。
Unveiling functions of the visual cortex using task-specific deep neural networks
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Abstract:
The human visual cortex enables visual perception through a cascade of hierarchical computations in cortical regions with distinct functionalities. Here, we introduce an AI-driven approach to discover the functional mapping of the visual cortex. We related human brain responses to scene images measured with functional MRI (fMRI) systematically to a diverse set of deep neural networks (DNNs) optimized to perform different scene perception tasks. We found a structured mapping between DNN tasks and brain regions along the ventral and dorsal visual streams. Low-level visual tasks mapped onto early brain regions, 3-dimensional scene perception tasks mapped onto the dorsal stream, and semantic tasks mapped onto the ventral stream. This mapping was of high fidelity, with more than 60% of the explainable variance in nine key regions being explained. Together, our results provide a novel functional mapping of the human visual cortex and demonstrate the power of the computational approach.
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