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Deep Neural Networks predict Hierarchical Spatio-temporal Cortical Dynamics of Human Visual Object Recognition.

, , , , and . CoRR, (2016)

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Identifying true cortical interactions in MEG using the nulling beamformer., , , and . NeuroImage, 49 (4): 3161-3174 (2010)Content-Dependent Fusion: Combining Human MEG and FMRI Data to Reveal Spatiotemporal Dynamics of Animacy and Real-world Object Size., , , and . AAAI Spring Symposia, AAAI Press, (2017)A novel ANCOVA design for analysis of MEG data with application to a visual attention study., , , , , and . NeuroImage, 44 (1): 164-174 (2009)Exploring Human Visual Attention in an Meg Study of a Spatial Cueing Paradigm Using a Novel Ancova Design., , , , , and . ISBI, page 1096-1099. IEEE, (2007)Partitioning directed graphs based on modularity and information flow., , and . ISBI, page 1105-1108. IEEE, (2011)Multi-view network module detection., and . ACSSC, page 975-979. IEEE, (2013)Tracking the Spatiotemporal Neural Dynamics of Real-world Object Size and Animacy in the Human Brain., , , and . J. Cognitive Neuroscience, (2018)Spatiotemporal Localization of Significant Activation in MEG Using Permutation Tests., , , and . IPMI, volume 2732 of Lecture Notes in Computer Science, page 512-523. Springer, (2003)Of the largest eigenvalue for modularity-based partitioning., , and . SSP, page 125-128. IEEE, (2012)A note on the phase locking value and its properties., , and . NeuroImage, (2013)