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利用任务特定的深度神经网络揭示视觉皮层的功能。

Unveiling functions of the visual cortex using task-specific deep neural networks.

机构信息

Department of Education and Psychology, Freie Universität Berlin, Germany.

Department of Computer Science, Goethe University, Frankfurt am Main, Germany.

出版信息

PLoS Comput Biol. 2021 Aug 13;17(8):e1009267. doi: 10.1371/journal.pcbi.1009267. eCollection 2021 Aug.

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.

摘要

人类视觉皮层通过皮质区域中具有不同功能的层次化计算级联来实现视觉感知。在这里,我们引入了一种由人工智能驱动的方法来发现视觉皮层的功能映射。我们将人类大脑对功能磁共振成像(fMRI)测量的场景图像的反应与经过优化以执行不同场景感知任务的各种深度神经网络(DNN)系统地联系起来。我们发现 DNN 任务与沿着腹侧和背侧视觉流的大脑区域之间存在结构化的映射。低水平视觉任务映射到早期大脑区域,3D 场景感知任务映射到背侧流,语义任务映射到腹侧流。这种映射具有很高的保真度,在九个关键区域中,超过 60%的可解释方差得到了解释。总之,我们的结果提供了人类视觉皮层的新的功能映射,并展示了计算方法的强大功能。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/10f8/8407579/3c6186a58fee/pcbi.1009267.g001.jpg

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