首页|Enhancing neural encoding models for naturalistic perception with a multi-level integration of deep neural networks and cortical networks
Enhancing neural encoding models for naturalistic perception with a multi-level integration of deep neural networks and cortical networks
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Cognitive neuroscience aims to develop computational models that can accurately predict and explain neural responses to sensory inputs in the cortex.Recent studies attempt to leverage the representation power of deep neural networks(DNNs)to predict the brain response and suggest a correspondence between artificial and biological neural networks in their feature representations.However,typical voxel-wise encoding models tend to rely on specific networks designed for computer vision tasks,leading to suboptimal brain-wide correspondence during cognitive tasks.To address this challenge,this work proposes a novel approach that upgrades voxel-wise encoding models through multi-level integration of features from DNNs and information from brain networks.Our approach combines DNN feature-level ensemble learning and brain atlas-level model integration,resulting in significant improvements in predicting whole-brain neural activity during naturalistic video perception.Furthermore,this multi-level integration framework enables a deeper understanding of the brain's neural representation mech-anism,accurately predicting the neural response to complex visual concepts.We demonstrate that neural encoding models can be optimized by leveraging a framework that integrates both data-driven approaches and theoretical insights into the functional structure of the cortical networks.
School of Biomedical Engineering & State Key Laboratory of Advanced Medical Materials and Devices,ShanghaiTech University,Shanghai 201210,China
School of Computer Science and Engineering,University of Electronic Science and Technology of China,Chengdu 611731,China
Department of Computer and Information Science,University of Pennsylvania,Philadelphia,PA 19104,USA
Shenzhen Institute for Advanced Study,University of Electronic Science and Technology of China,Shenzhen 518110,China
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National Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaShenzhen Science and Technology ProgramShanghai Pujiang Program
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