A visual encoding model based on deep neural networks and transfer learning for brain activity measured by functional magnetic resonance imaging
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Li Tong | Bin Yan | Kai Qiao | Guoen Hu | Chi Zhang | Linyuan Wang | Ruyuan Zhang | Linyuan Wang | Ruyuan Zhang | Guoen Hu | Li Tong | Bin Yan | Chi Zhang | Kai Qiao
[1] Jack L. Gallant,et al. Encoding and decoding in fMRI , 2011, NeuroImage.
[2] Qiang Yang,et al. A Survey on Transfer Learning , 2010, IEEE Transactions on Knowledge and Data Engineering.
[3] Chi Zhang,et al. Constraint-Free Natural Image Reconstruction From fMRI Signals Based on Convolutional Neural Network , 2018, Front. Hum. Neurosci..
[4] Yuan Yu,et al. TensorFlow: A system for large-scale machine learning , 2016, OSDI.
[5] Marcel van Gerven,et al. New advances in encoding and decoding of brain signals , 2018, NeuroImage.
[6] Junxing Shi,et al. Deep Residual Network Predicts Cortical Representation and Organization of Visual Features for Rapid Categorization , 2018, Scientific Reports.
[7] Ha Hong,et al. Explicit information for category-orthogonal object properties increases along the ventral stream , 2016, Nature Neuroscience.
[8] J. P. Jones,et al. An evaluation of the two-dimensional Gabor filter model of simple receptive fields in cat striate cortex. , 1987, Journal of neurophysiology.
[9] Marcel A. J. van Gerven,et al. Deep Neural Networks Reveal a Gradient in the Complexity of Neural Representations across the Ventral Stream , 2014, The Journal of Neuroscience.
[10] Yizhen Zhang,et al. Variational Autoencoder: An Unsupervised Model for Modeling and Decoding fMRI Activity in Visual Cortex , 2017, bioRxiv.
[11] Luigi Acerbi,et al. Advances in Neural Information Processing Systems 27 , 2014 .
[12] J. DiCarlo,et al. Using goal-driven deep learning models to understand sensory cortex , 2016, Nature Neuroscience.
[13] Junwei Han,et al. Survey of encoding and decoding of visual stimulus via FMRI: an image analysis perspective , 2013, Brain Imaging and Behavior.
[14] Li Tong,et al. Category Decoding of Visual Stimuli From Human Brain Activity Using a Bidirectional Recurrent Neural Network to Simulate Bidirectional Information Flows in Human Visual Cortices , 2019, Front. Neurosci..
[15] Deanna Needell,et al. Signal Recovery From Incomplete and Inaccurate Measurements Via Regularized Orthogonal Matching Pursuit , 2007, IEEE Journal of Selected Topics in Signal Processing.
[16] Kendrick N. Kay,et al. Principles for models of neural information processing , 2017, NeuroImage.
[17] J. Gallant,et al. Identifying natural images from human brain activity , 2008, Nature.
[18] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[19] Chi Zhang,et al. Accurate Reconstruction of Image Stimuli From Human Functional Magnetic Resonance Imaging Based on the Decoding Model With Capsule Network Architecture , 2018, Front. Neuroinform..
[20] Li Fei-Fei,et al. ImageNet: A large-scale hierarchical image database , 2009, CVPR.
[21] E H Adelson,et al. Spatiotemporal energy models for the perception of motion. , 1985, Journal of the Optical Society of America. A, Optics and image science.
[22] Ryan J. Prenger,et al. Bayesian Reconstruction of Natural Images from Human Brain Activity , 2009, Neuron.
[23] Deanna Needell,et al. Uniform Uncertainty Principle and Signal Recovery via Regularized Orthogonal Matching Pursuit , 2007, Found. Comput. Math..
[24] Ha Hong,et al. Performance-optimized hierarchical models predict neural responses in higher visual cortex , 2014, Proceedings of the National Academy of Sciences.
[25] Nicole C. Rust,et al. Do We Know What the Early Visual System Does? , 2005, The Journal of Neuroscience.
[26] Jack L. Gallant,et al. A Continuous Semantic Space Describes the Representation of Thousands of Object and Action Categories across the Human Brain , 2012, Neuron.
[27] Yizhen Zhang,et al. Deep Recurrent Neural Network Reveals a Hierarchy of Process Memory during Dynamic Natural Vision , 2017, bioRxiv.
[28] Marcel A. J. van Gerven,et al. A primer on encoding models in sensory neuroscience , 2017 .
[29] Bryan Tripp. A deeper understanding of the brain , 2018, NeuroImage.
[30] Yoshua Bengio,et al. How transferable are features in deep neural networks? , 2014, NIPS.
[31] Xiao-Jing Wang,et al. Task representations in neural networks trained to perform many cognitive tasks , 2019, Nature Neuroscience.
[32] Michael I. Jordan,et al. Advances in Neural Information Processing Systems 30 , 1995 .