Deep learning as a computational model in functional neuroimaging
Deep learning as a computational model in functional neuroimaging
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深度学习作为功能神经影像的计算模型
DOI：
10.11225/cs.2021.060
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发表时间：
2022
期刊：
影响因子：
--
通讯作者：
田中 政輝
中科院分区：
文献类型：
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作者：
小川 昭利;田中 政輝
功能神经影像 (FNI) 在研究认知机制中的信息过程的认知科学中发挥着重要作用。解释行为及其底层信息处理的计算模型对于 FNI 领域的认知功能映射已变得不可或缺。然而，使用由简单方程和多个参数组成的计算模型来揭示大脑中信息处理的分布式表示是具有挑战性的。机器学习分析了大脑信息处理的激活模式。甚至在深度学习革命之前，机器学习就被用来根据刺激预测大脑活动模式（即编码），并根据大脑活动区分或重建刺激和行为（即解码）。卷积神经网络 (CNN) 是模仿视觉神经系统进行物体识别的深度神经网络 (DNN) 之一，是深度学习作为大脑计算模型潜力的开创性例子。 CNN 中间层的活动可以反映腹侧视觉通路中物体识别的分布式过程。为了使用 DNN 作为 FNI 的计算模型来实现更广泛的感知和认知，DNN 中间层的活动应该对应于大脑区域的激活。本文简要回顾了 FNI 和 FNI 机器学习中包含的深度学习的计算模型，并讨论了 DNN 作为 FNI 中的计算模型。我们建议深度学习可以作为 FNI 中的计算模型，将大脑中的激活模式与分层/分布式认知过程联系起来。
Functional neuroimaging (FNI) plays an essential role in cognitive science investigating information processes in cognitive mechanisms. Computational models that explain the behavior and its underlying information processing have become indispensable for the functional mapping of cognition in the FNI field. However, it is challenging to use computational models consisting of simple equations and several parameters to reveal the distributed representation of information processing in the brain. Machine learning has analyzed the activation pattern for information processing in the brain. Even before the deep learning revolution, machine learning was used to predict brain activity patterns from stimuli (ie, encoding) and to discriminate or reconstruct the stimuli and behavior from brain activity (ie, decoding). Convolutional neural network (CNN), one of the deep neural networks (DNNs) mimicking the visual nervous system for object recognition, was a pioneering example of the potential of deep learning as a computational model of the brain. The activity of the middle layers of CNN can reflect distributed processes for object recognition in the ventral visual pathway. To use DNNs as computational models of FNI for more broad perceptions and cognitions, the activity of the middle layer of DNN should correspond to the activation of a brain region. This article briefly reviews the computational models of FNI and deep learning included in FNI machine learning and discusses the DNN as a computational model in FNI. We suggest that deep learning can serve as a computational model in FNI, connecting the activation pattern in the brain and hierarchical/distributed cognitive processes.