Deep Neural Networks Reveal a Gradient in the Complexity of Neural Representations across the Ventral Stream
Deep Neural Networks Reveal a Gradient in the Complexity of Neural Representations across the Ventral Stream
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DOI：
10.1523/jneurosci.5023-14.2015
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发表时间：
2015-07-08
影响因子：
5.3
通讯作者：
van Gerven, Marcel A. J.
中科院分区：
文献类型：
--
作者：
Guclu, Umut;van Gerven, Marcel A. J.
收敛的证据表明，灵长类动物腹侧视觉通路编码越来越复杂的刺激功能在下游地区。我们定量地表明，确实存在一个明确的梯度功能复杂性在人类大脑的腹侧通路。这是通过使用深度神经网络在皮层片上映射数千个复杂性不断增加的刺激特征来实现的。我们的方法还揭示了一个细粒度的功能专业化的腹侧流的下游地区。此外，它允许以无与伦比的准确度从人脑活动中解码表征，证实了所开发方法的质量。刺激功能，成功地解释了神经反应表明，人口感受野明确调整对象分类。这为以下假设提供了强有力的支持：对象分类是灵长类动物腹侧流功能组织的指导原则。
Converging evidence suggests that the primate ventral visual pathway encodes increasingly complex stimulus features in downstream areas. We quantitatively show that there indeed exists an explicit gradient for feature complexity in the ventral pathway of the human brain. This was achieved by mapping thousands of stimulus features of increasing complexity across the cortical sheet using a deep neural network. Our approach also revealed a fine-grained functional specialization of downstream areas of the ventral stream. Furthermore, it allowed decoding of representations from human brain activity at an unsurpassed degree of accuracy, confirming the quality of the developed approach. Stimulus features that successfully explained neural responses indicate that population receptive fields were explicitly tuned for object categorization. This provides strong support for the hypothesis that object categorization is a guiding principle in the functional organization of the primate ventral stream.