Kuzovkin Ilya, Vicente Raul, Petton Mathilde, Lachaux Jean-Philippe, Baciu Monica, Kahane Philippe, Rheims Sylvain, Vidal Juan R, Aru Jaan
Computational Neuroscience Lab, Institute of Computer Science, University of Tartu, Tartu, 51005, Estonia.
INSERM U1028, CNRS UMR5292, Brain Dynamics and Cognition Team, Lyon Neuroscience Research Center, Bron, 69500, France.
Commun Biol. 2018 Aug 8;1:107. doi: 10.1038/s42003-018-0110-y. eCollection 2018.
Recent advances in the field of artificial intelligence have revealed principles about neural processing, in particular about vision. Previous work demonstrated a direct correspondence between the hierarchy of the human visual areas and layers of deep convolutional neural networks (DCNN) trained on visual object recognition. We use DCNN to investigate which frequency bands correlate with feature transformations of increasing complexity along the ventral visual pathway. By capitalizing on intracranial depth recordings from 100 patients we assess the alignment between the DCNN and signals at different frequency bands. We find that gamma activity (30-70 Hz) matches the increasing complexity of visual feature representations in DCNN. These findings show that the activity of the DCNN captures the essential characteristics of biological object recognition not only in space and time, but also in the frequency domain. These results demonstrate the potential that artificial intelligence algorithms have in advancing our understanding of the brain.
人工智能领域的最新进展揭示了有关神经处理的原理，尤其是视觉方面的原理。先前的研究表明，人类视觉区域的层次结构与用于视觉物体识别训练的深度卷积神经网络（DCNN）的层之间存在直接对应关系。我们使用DCNN来研究哪些频段与沿腹侧视觉通路复杂度不断增加的特征变换相关。通过利用100名患者的颅内深度记录，我们评估了DCNN与不同频段信号之间的一致性。我们发现，伽马活动（30 - 70赫兹）与DCNN中视觉特征表示的复杂度增加相匹配。这些发现表明，DCNN的活动不仅在空间和时间上，而且在频域中捕捉了生物物体识别的基本特征。这些结果证明了人工智能算法在推进我们对大脑理解方面的潜力。
