M
Michael Eickenberg
Researcher at University of California, Berkeley
Publications - 65
Citations - 2800
Michael Eickenberg is an academic researcher from University of California, Berkeley. The author has contributed to research in topics: Computer science & Artificial neural network. The author has an hindex of 15, co-authored 45 publications receiving 1907 citations. Previous affiliations of Michael Eickenberg include École Polytechnique & French Institute for Research in Computer Science and Automation.
Papers
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Journal ArticleDOI
Machine learning for neuroimaging with scikit-learn.
Alexandre Abraham,Alexandre Abraham,Fabian Pedregosa,Fabian Pedregosa,Michael Eickenberg,Michael Eickenberg,Philippe Gervais,Philippe Gervais,Andreas Mueller,Jean Kossaifi,Alexandre Gramfort,Alexandre Gramfort,Alexandre Gramfort,Bertrand Thirion,Bertrand Thirion,Gaël Varoquaux,Gaël Varoquaux +16 more
TL;DR: It is illustrated how scikit-learn, a Python machine learning library, can be used to perform some key analysis steps and its application to neuroimaging data provides a versatile tool to study the brain.
Journal ArticleDOI
Seeing it all: Convolutional network layers map the function of the human visual system
Michael Eickenberg,Michael Eickenberg,Michael Eickenberg,Alexandre Gramfort,Gaël Varoquaux,Bertrand Thirion,Bertrand Thirion +6 more
TL;DR: A full brain predictive model synthesizes brain maps for other visual experiments and recovers the activations observed in the corresponding fMRI studies, showing that this deep encoding model captures representations of brain function that are universal across experimental paradigms.
Posted Content
Machine Learning for Neuroimaging with Scikit-Learn
Alexandre Abraham,Fabian Pedregosa,Michael Eickenberg,Philippe Gervais,Andreas Müller,Jean Kossaifi,Alexandre Gramfort,Bertrand Thirion,Gaël Varoquaux +8 more
TL;DR: Scikit-learn as mentioned in this paper contains a very large set of statistical learning algorithms, both supervised and unsupervised, and its application to neuroimaging data provides a versatile tool to study the brain.
Journal ArticleDOI
Formal Models of the Network Co-occurrence Underlying Mental Operations.
TL;DR: A multivariate statistical learning approach that can extract the major brain networks and quantify their configuration during various psychological tasks and demonstrates that task-specific neural activity patterns can be explained by plausible combinations of resting-state networks.
Journal ArticleDOI
Solid harmonic wavelet scattering for predictions of molecule properties.
TL;DR: In this article, a machine learning algorithm for the prediction of molecule properties inspired by ideas from density functional theory (DFT) is presented. But their approach is limited to a single set of scattering coefficients.