相关参考文献
注意：仅列出部分参考文献，下载原文获取全部文献信息。Convolutional Neural Networks as a Model of the Visual System: Past, Present, and Future
Grace W. Lindsay
JOURNAL OF COGNITIVE NEUROSCIENCE (2021)
Surface-based analysis increases the specificity of cortical activation patterns and connectivity results
Stefan Brodoehl et al.
SCIENTIFIC REPORTS (2020)
A neural network trained for prediction mimics diverse features of biological neurons and perception
William Lotter et al.
NATURE MACHINE INTELLIGENCE (2020)
Emerging Opportunities for Advancing Cognitive Neuroscience
Uri Hasson et al.
TRENDS IN COGNITIVE SCIENCES (2019)
Deep neural network models of sensory systems: windows onto the role of task constraints
Alexander J. E. Kell et al.
CURRENT OPINION IN NEUROBIOLOGY (2019)
Interpreting encoding and decoding models
Nikolaus Kriegeskorte et al.
CURRENT OPINION IN NEUROBIOLOGY (2019)
Ciftify: A framework for surface-based analysis of legacy MR acquisitions
Erin W. Dickie et al.
NEUROIMAGE (2019)
Deep Neural Networks as Scientific Models
Radoslaw M. Cichy et al.
TRENDS IN COGNITIVE SCIENCES (2019)
BOLD5000, a public fMRI dataset while viewing 5000 visual images
Nadine Chang et al.
SCIENTIFIC DATA (2019)
Number detectors spontaneously emerge in a deep neural network designed for visual object recognition
Khaled Nasr et al.
SCIENCE ADVANCES (2019)
EEG-BIDS, an extension to the brain imaging data structure for electroencephalography
Cyril R. Pernet et al.
SCIENTIFIC DATA (2019)
Similarity judgments and cortical visual responses reflect different properties of object and scene categories in naturalistic images
Marcie L. King et al.
NEUROIMAGE (2019)
Deep Learning: The Good, the Bad, and the Ugly
Thomas Serre
ANNUAL REVIEW OF VISION SCIENCE, VOL 5 (2019)
A deep learning framework for neuroscience
Blake A. Richards et al.
NATURE NEUROSCIENCE (2019)
fMRIPrep: a robust preprocessing pipeline for functional MRI
Oscar Esteban et al.
NATURE METHODS (2019)
Deep image reconstruction from human brain activity
Guohua Shen et al.
PLOS COMPUTATIONAL BIOLOGY (2019)
Reconstructing faces from fMRI patterns using deep generative neural networks
Rufin VanRullen et al.
COMMUNICATIONS BIOLOGY (2019)
Methods for interpreting and understanding deep neural networks
Gregoire Montavon et al.
DIGITAL SIGNAL PROCESSING (2018)
Large-Scale, High-Resolution Comparison of the Core Visual Object Recognition Behavior of Humans, Monkeys, and State-of-the-Art Deep Artificial Neural Networks
Rishi Rajalingham et al.
JOURNAL OF NEUROSCIENCE (2018)
Using human brain activity to guide machine learning
Ruth C. Fong et al.
SCIENTIFIC REPORTS (2018)
Comment: MEG-BIDS, the brain imaging data structure extended to magnetoencephalography
Guiomar Niso et al.
SCIENTIFIC DATA (2018)
Predicting eye movement patterns from fMRI responses to natural scenes
Thomas P. O'Connell et al.
NATURE COMMUNICATIONS (2018)
Deep convolutional networks do not classify based on global object shape
Nicholas Baker et al.
PLOS COMPUTATIONAL BIOLOGY (2018)
'Artiphysiology' reveals V4-like shape tuning in a deep network trained for image classification
Dean A. Pospisil et al.
ELIFE (2018)
Neural Encoding and Decoding with Deep Learning for Dynamic Natural Vision
Haiguang Wen et al.
CEREBRAL CORTEX (2018)
Illusory Motion Reproduced by Deep Neural NetworksTrained for Prediction
Eiji Watanabe et al.
FRONTIERS IN PSYCHOLOGY (2018)
Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review
Waseem Rawat et al.
NEURAL COMPUTATION (2017)
Seeing it all: Convolutional network layers map the function of the human visual system
Michael Eickenberg et al.
NEUROIMAGE (2017)
Deep Convolutional Neural Networks Outperform Feature-Based But Not Categorical Models in Explaining Object Similarity Judgments
Kamila M. Jozwik et al.
FRONTIERS IN PSYCHOLOGY (2017)
A multi-modal parcellation of human cerebral cortex
Matthew F. Glasser et al.
NATURE (2016)
Using goal-driven deep learning models to understand sensory cortex
Daniel L. K. Yamins et al.
NATURE NEUROSCIENCE (2016)
Representational Distance Learning for Deep Neural Networks
Patrick McClure et al.
FRONTIERS IN COMPUTATIONAL NEUROSCIENCE (2016)
Pyrcca: Regularized Kernel Canonical Correlation Analysis in Python and Its Application to Neuroimaging
Natalia Y. Bilenko et al.
FRONTIERS IN NEUROINFORMATICS (2016)
Deep Neural Networks Reveal a Gradient in the Complexity of Neural Representations across the Ventral Stream
Umut Guclu et al.
JOURNAL OF NEUROSCIENCE (2015)
Performance-optimized hierarchical models predict neural responses in higher visual cortex
Daniel L. K. Yamins et al.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2014)
Deep Supervised, but Not Unsupervised, Models May Explain IT Cortical Representation
Seyed-Mahdi Khaligh-Razavi et al.
PLOS COMPUTATIONAL BIOLOGY (2014)
The minimal preprocessing pipelines for the Human Connectome Project
Matthew F. Glasser et al.
NEUROIMAGE (2013)
Deep Neural Networks for Acoustic Modeling in Speech Recognition
Geoffrey Hinton et al.
IEEE SIGNAL PROCESSING MAGAZINE (2012)
Analysis of functional magnetic resonance imaging in Python
K. Jarrod Millman et al.
COMPUTING IN SCIENCE & ENGINEERING (2007)
分享论文
