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Deep Neural Networks predict Hierarchical Spatio-temporal Cortical Dynamics of Human Visual Object Recognition
TLDR
It was shown that the DNN captured the stages of human visual processing in both time and space from early visual areas towards the dorsal and ventral streams and provided an algorithmically informed view on the spatio-temporal dynamics of visual object recognition in the human visual brain.Abstract:
The complex multi-stage architecture of cortical visual pathways provides the neural basis for efficient visual object recognition in humans. However, the stage-wise computations therein remain poorly understood. Here, we compared temporal (magnetoencephalography) and spatial (functional MRI) visual brain representations with representations in an artificial deep neural network (DNN) tuned to the statistics of real-world visual recognition. We showed that the DNN captured the stages of human visual processing in both time and space from early visual areas towards the dorsal and ventral streams. Further investigation of crucial DNN parameters revealed that while model architecture was important, training on real-world categorization was necessary to enforce spatio-temporal hierarchical relationships with the brain. Together our results provide an algorithmically informed view on the spatio-temporal dynamics of visual object recognition in the human visual brain.read more
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Journal ArticleDOI
Neural Encoding and Decoding with Deep Learning for Dynamic Natural Vision.
TL;DR: It is shown that such CNN could reliably predict and decode functional magnetic resonance imaging data from humans watching natural movies, despite its lack of any mechanism to account for temporal dynamics or feedback processing.
Journal ArticleDOI
Computational approaches to fMRI analysis.
Jonathan D. Cohen,Nathaniel D. Daw,Barbara E. Engelhardt,Uri Hasson,Kai Li,Yael Niv,Kenneth A. Norman,Jonathan W. Pillow,Peter J. Ramadge,Nicholas B. Turk-Browne,Theodore L. Willke +10 more
TL;DR: These methods highlight the importance of computational techniques in fMRI analysis, especially machine learning, algorithmic optimization and parallel computing, and adoption of these techniques is enabling a new generation of experiments and analyses that could transform the understanding of some of the most complex—and distinctly human—signals in the brain.
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Brain Mechanisms Underlying the Brief Maintenance of Seen and Unseen Sensory Information
TL;DR: The neural dynamics underlying the maintenance of variably visible stimuli using magnetoencephalography are investigated and it is suggested that invisible information can be briefly maintained within the higher processing stages of visual perception.
Reference EntryDOI
Deep Neural Networks in Computational Neuroscience
TL;DR: DNNs represent a powerful framework for building task-performing models and will drive substantial insights in computational neuroscience.
Posted ContentDOI
CORnet: Modeling the Neural Mechanisms of Core Object Recognition
Jonas Kubilius,Jonas Kubilius,Martin Schrimpf,Aran Nayebi,Daniel M. Bear,Yamins Dlk,James J. DiCarlo,James J. DiCarlo +7 more
TL;DR: The current best ANN model derived from this approach (CORnet-S) is among the top models on Brain-Score, a composite benchmark for comparing models to the brain, but is simpler than other deep ANNs in terms of the number of convolutions performed along the longest path of information processing in the model.
References
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Proceedings Article
ImageNet Classification with Deep Convolutional Neural Networks
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Proceedings ArticleDOI
ImageNet: A large-scale hierarchical image database
TL;DR: A new database called “ImageNet” is introduced, a large-scale ontology of images built upon the backbone of the WordNet structure, much larger in scale and diversity and much more accurate than the current image datasets.
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Deep learning
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ImageNet Large Scale Visual Recognition Challenge
Olga Russakovsky,Jia Deng,Hao Su,Jonathan Krause,Sanjeev Satheesh,Sean Ma,Zhiheng Huang,Andrej Karpathy,Aditya Khosla,Michael S. Bernstein,Alexander C. Berg,Li Fei-Fei +11 more
TL;DR: The ImageNet Large Scale Visual Recognition Challenge (ILSVRC) as mentioned in this paper is a benchmark in object category classification and detection on hundreds of object categories and millions of images, which has been run annually from 2010 to present, attracting participation from more than fifty institutions.
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