The functional neuroanatomy of face perception: from brain measurements to deep neural networks.
The functional neuroanatomy of face perception: from brain measurements to deep neural networks.
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DOI：
10.1098/rsfs.2018.0013
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发表时间：
2018-08-06
期刊：
影响因子：
4.4
通讯作者：
Natu VS
中科院分区：
文献类型：
--
作者：
Grill-Spector K;Weiner KS;Gomez J;Stigliani A;Natu VS
神经科学的一个中心目标是了解腹侧视觉流中的处理如何实现快速而稳健的感知和识别。最近的神经科学发现极大地促进了对腹侧视觉流的功能、结构和计算的理解，腹侧视觉流是支持这种行为的基础设施。与此同时，分层深度神经网络 (DNN) 等计算模型的重大进步使机器性能达到了与人类性能相当的水平。在这里，我们提出了一个使用腹侧面部网络作为模型系统的新框架，以说明提高现有 DNN 的神经准确性如何允许研究人员测试人脑功能架构的计算优势。因此，该综述 (i) 考虑了腹侧面部网络的具体神经实现特征，(ii) 描述了大脑和 DNN 功能架构之间的相似性和差异，(iii) 为大脑内可能提高 DNN 性能的实现特征的计算价值提供了假设。重要的是，这个新框架促进了神经科学发现与 DNN 的结合，以测试视觉系统基本组织特征的计算优势。
A central goal in neuroscience is to understand how processing within the ventral visual stream enables rapid and robust perception and recognition. Recent neuroscientific discoveries have significantly advanced understanding of the function, structure and computations along the ventral visual stream that serve as the infrastructure supporting this behaviour. In parallel, significant advances in computational models, such as hierarchical deep neural networks (DNNs), have brought machine performance to a level that is commensurate with human performance. Here, we propose a new framework using the ventral face network as a model system to illustrate how increasing the neural accuracy of present DNNs may allow researchers to test the computational benefits of the functional architecture of the human brain. Thus, the review (i) considers specific neural implementational features of the ventral face network, (ii) describes similarities and differences between the functional architecture of the brain and DNNs, and (iii) provides a hypothesis for the computational value of implementational features within the brain that may improve DNN performance. Importantly, this new framework promotes the incorporation of neuroscientific findings into DNNs in order to test the computational benefits of fundamental organizational features of the visual system.
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影响因子：
5.7
作者：
Ackman JB;Crair MC
通讯作者：
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影响因子：
3.1
作者：
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作者：
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通讯作者：
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作者：
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通讯作者：
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