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Exploring the Brain-like Properties of Deep Neural networks:A Neural Encoding Perspective

Machine Intelligence Research 2022年第5期19卷 439-455页

作者： Qiongyi Zhou Changde Du Huiguang He Research Center for Brain-inspired Intelligence and National Laboratory of Pattern Recognition Institute of AutomationChinese Academy of SciencesBeijing 100190China School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100190China Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of SciencesBeijing 100190China

Nowadays,deep neural networks(DNNs)have been equipped with powerful representation *** deep convolutional neural networks(CNNs)that draw inspiration from the visual processing mechanism of the primate early visual cortex have outperformed humans on object categorization and have been found to possess many brain-like ***,vision transformers(ViTs)have been striking paradigms of DNNs and have achieved remarkable improvements on many vision tasks compared to *** is natural to ask how the brain-like properties of ViTs *** the model paradigm,we are also interested in the effects of factors,such as model size,multimodality,and temporality,on the ability of networks to model the human visual pathway,especially when considering that existing research has been limited to *** this paper,we systematically evaluate the brain-like properties of 30 kinds of computer vision models varying from CNNs and ViTs to their hybrids from the perspective of explaining brain activities of the human visual cortex triggered by dynamic *** on two neural datasets demonstrate that neither CNN nor transformer is the optimal model paradigm for modelling the human visual *** reveal hierarchical correspondences to the visual pathway as CNNs ***,we find that multi-modal and temporal networks can better explain the neural activities of large parts of the visual cortex,whereas a larger model size is not a sufficient condition for bridging the gap between human vision and artificial *** study sheds light on the design principles for more brain-like *** code is available at https://***/QYiZhou/LWNeuralEncoding.

关键词： Convolutional neural network(CNN) vision transformer(Vi T) multi-modal networks spatial-temporal networks visual neural encoding brain-like neural networks

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