Passage of an Iron Rod Through the Head, The Journal of Neuropsychiatry and Clinical Neurosciences, vol.11, issue.2, pp.389-393 ,
DOI : 10.1176/jnp.11.2.281
Vergleichende Lokalisationslehre der Großhirnrinde, 1909. ,
THE BRAIN???S RECORD OF AUDITORY AND VISUAL EXPERIENCE, Brain, vol.86, issue.4, pp.595-696, 1963. ,
DOI : 10.1093/brain/86.4.595
Principles of psychopharmacology: a textbook for physicians, medical students, and behavioral scientists, 1970. ,
Tetramethyl benzidine for horseradish peroxidase neurohistochemistry: a non-carcinogenic blue reaction product with superior sensitivity for visualizing neural afferents and efferents., Journal of Histochemistry & Cytochemistry, vol.26, issue.2, pp.106-123, 1978. ,
DOI : 10.1177/26.2.24068
Current concepts in neuroanatomical tracing, Progress in Neurobiology, vol.62, issue.4, pp.327-351, 2000. ,
DOI : 10.1016/S0301-0082(00)00019-8
The evolving landscape of human cortical connectivity: Facts and inferences, NeuroImage, vol.62, issue.4, pp.2182-2191, 2012. ,
DOI : 10.1016/j.neuroimage.2011.12.033
The future of human cerebral cartography: a novel approach, Philosophical Transactions of the Royal Society of London B: Biological Sciences, p.20140171, 1668. ,
DOI : 10.1126/science.11642769
Neuroscience thinks big (and collaboratively), Nature Reviews Neuroscience, vol.14, issue.9, pp.659-664, 2013. ,
DOI : 10.1126/science.1236939
BigBrain: An Ultrahigh-Resolution 3D Human Brain Model, Science, vol.340, issue.6139, pp.1472-1477, 2013. ,
DOI : 10.1126/science.1235381
The Human Connectome Project: A data acquisition perspective, NeuroImage, vol.62, issue.4, pp.2222-2253, 2012. ,
DOI : 10.1016/j.neuroimage.2012.02.018
Computer-Age Statistical Inference. 2016. 13 Making big data open: data sharing in neuroimaging, Nat Neurosci, issue.11, pp.17-1510, 2014. ,
The big data dilemma, UK: Committee on Applied and Theoretical Statistics, 2016. ,
Frontiers in Massive Data Analysis, 2013. ,
Big data: The next frontier for innovation, competition, and productivity, 2011. ,
Whole-brain activity mapping onto a zebrafish brain atlas, Nature Methods, vol.126, issue.11, pp.1039-1046, 2015. ,
DOI : 10.1002/hbm.1024
The big data challenges of connectomics, Nature Neuroscience, vol.27, issue.11, pp.1448-1454, 2014. ,
DOI : 10.1016/j.conb.2008.08.010
Probabilistic machine learning and artificial intelligence, Nature, vol.80, issue.7553, pp.521-452, 2015. ,
DOI : 10.1109/5.18626
Gaussian Processes in Machine Learning, 2006. ,
DOI : 10.1162/089976602317250933
URL : http://hdl.handle.net/11858/00-001M-0000-0013-F365-A
Explaining Missing Heritability Using Gaussian Process Regression. bioRxiv, p.40576, 2016. ,
DOI : 10.1101/040576
Kernel methods in machine learning. The annals of statistics, pp.1171-1220, 2008. ,
Does cognitive science need kernels? Trends in cognitive sciences, pp.381-388, 2009. ,
The Unreasonable Effectiveness of Data, IEEE Intelligent Systems, vol.24, issue.2, pp.8-12, 2009. ,
DOI : 10.1109/MIS.2009.36
A Large-Scale Model of the Functioning Brain, Science, vol.338, issue.6111, pp.1202-1205, 2012. ,
DOI : 10.1126/science.1225266
Machine learning: Trends, perspectives, and prospects, Science, vol.349, issue.6245, pp.255-260, 2015. ,
DOI : 10.1126/science.aaa8415
CONTRIBUTION TO THE STUDY OF THE CORTICAL SENSORY AREAS, Brain, vol.24, issue.3, pp.430-452, 1901. ,
DOI : 10.1093/brain/24.3.430
Functional Specialization and Flexibility in Human Association Cortex, Cerebral Cortex, vol.25, issue.10, pp.3654-72, 2015. ,
DOI : 10.1093/cercor/bhu217
The modular and integrative functional architecture of the human brain, Proceedings of the National Academy of Sciences, vol.112, issue.49, pp.112-6798 ,
DOI : 10.1073/pnas.1510619112
Generative Embedding for Model-Based Classification of fMRI Data, PLoS Computational Biology, vol.49, issue.6, p.1002079, 2011. ,
DOI : 10.1371/journal.pcbi.1002079.s004
Modeling 3D Facial Shape from DNA, PLoS Genetics, vol.46, issue.3, pp.1004224-1004257, 2014. ,
DOI : 10.1371/journal.pgen.1004224.s049
Deep Neural Networks Reveal a Gradient in the Complexity of Neural Representations across the Ventral Stream, Journal of Neuroscience, vol.35, issue.27, pp.35-10005, 2015. ,
DOI : 10.1523/JNEUROSCI.5023-14.2015
Inverse retinotopy: Inferring the visual content of images from brain activation patterns, NeuroImage, vol.33, issue.4, pp.1104-1120, 2006. ,
DOI : 10.1016/j.neuroimage.2006.06.062
URL : https://hal.archives-ouvertes.fr/hal-00349668
Reconstructing Visual Experiences from Brain Activity Evoked by Natural Movies, Current Biology, vol.21, issue.19, pp.1641-1647, 2011. ,
DOI : 10.1016/j.cub.2011.08.031
Natural speech reveals the semantic maps that tile human cerebral cortex, Nature, vol.9, issue.7600, pp.453-458, 2016. ,
DOI : 10.1038/nature17637
Deep learning in neural networks: An overview, Neural Networks, vol.61, pp.85-117, 2015. ,
DOI : 10.1016/j.neunet.2014.09.003
Deep learning, Nature, vol.9, issue.7553, pp.521-436, 2015. ,
DOI : 10.1007/s10994-013-5335-x
Neural networks and principal component analysis: Learning from examples without local minima, Neural Networks, vol.2, issue.1, pp.53-58, 1989. ,
DOI : 10.1016/0893-6080(89)90014-2
ICA with reconstruction cost for efficient overcomplete feature learning, Advances in Neural Information Processing Systems, 2011. ,
Semi-Supervised Factored Logistic Regression for High-Dimensional Neuroimaging Data, Advances in Neural Information Processing Systems, 2015. ,
URL : https://hal.archives-ouvertes.fr/hal-01211248
Translational Perspectives for Computational Neuroimaging, Neuron, vol.87, issue.4, pp.716-732, 2015. ,
DOI : 10.1016/j.neuron.2015.07.008
Can neuroscience be integrated into the DSM-V?, Nature Reviews Neuroscience, vol.148, issue.9, pp.725-757, 2007. ,
DOI : 10.1016/j.neuron.2005.09.025
Human-level concept learning through probabilistic program induction, Science, vol.350, issue.6266, pp.350-1332, 2015. ,
DOI : 10.1126/science.aab3050
Formal Models of the Network Co-occurrence Underlying Mental Operations, PLOS Computational Biology, vol.27, issue.8 ,
DOI : 10.1371/journal.pcbi.1004994.s012
URL : https://hal.archives-ouvertes.fr/hal-01338307
Altered global brain signal in schizophrenia, Proceedings of the National Academy of Sciences, vol.111, issue.20, pp.7438-7443 ,
DOI : 10.1073/pnas.1405289111
Modern science and the Bayesian-frequentist controversy, 2005. ,
Some issues in the foundation of statistics, Foundations of Science, vol.90, issue.no. 4, pp.19-39, 1995. ,
DOI : 10.1007/BF00208723
Classical and Bayesian Inference in Neuroimaging: Applications, NeuroImage, vol.16, issue.2, pp.484-512, 2002. ,
DOI : 10.1006/nimg.2002.1091
Bayesian model selection for group studies, NeuroImage, vol.46, issue.4, pp.1004-1021, 2009. ,
DOI : 10.1016/j.neuroimage.2009.03.025
Variational Bayesian inference for fMRI time series, NeuroImage, vol.19, issue.3, pp.727-768, 2003. ,
DOI : 10.1016/S1053-8119(03)00071-5
Variational Bayesian mixed-effects inference for classification studies, NeuroImage, vol.76, issue.76, pp.345-61, 2013. ,
DOI : 10.1016/j.neuroimage.2013.03.008
Bayesian inference with probabilistic population codes, Nature Neuroscience, vol.9, issue.11, pp.1432-1438, 2006. ,
DOI : 10.1038/nn1691
Identification of a Common Neurobiological Substrate for Mental Illness, JAMA Psychiatry, vol.72, issue.4, 2015. ,
DOI : 10.1001/jamapsychiatry.2014.2206
Bayesian nonparametric models, in Encyclopedia of Machine Learning, pp.81-89, 2011. ,
The Future of Psychology: Connecting Mind to Brain, Perspectives on Psychological Science, vol.316, issue.4, pp.326-365, 2009. ,
DOI : 10.1037/0003-066X.56.9.717
How to Grow a Mind: Statistics, Structure, and Abstraction, Science, vol.331, issue.6022, pp.331-1279, 2011. ,
DOI : 10.1126/science.1192788
Charting the landscape of priority problems in psychiatry, part 2: pathogenesis and aetiology. The Lancet Psychiatry, 2015. ,
Discovering Relations Between Mind, Brain, and Mental Disorders Using Topic Mapping, PLoS Computational Biology, vol.8, issue.10, p.1002707, 2012. ,
DOI : 10.1371/journal.pcbi.1002707.s002
The modular and integrative functional architecture of the human brain, Proceedings of the National Academy of Sciences, vol.112, issue.49 ,
DOI : 10.1073/pnas.1510619112
Bayesian model reveals latent atrophy factors with dissociable cognitive trajectories in Alzheimer's disease ,
Learning systems of concepts with an infinite relational model. in Aaai, 2006. ,
Infinite latent feature models and the Indian buffet process. in Advances in neural information processing systems, 2005. ,
Sharing Clusters Among Related Groups: Hierarchical Dirichlet Processes Advances in neural information processing systems, 2005. ,
Machine learning: a probabilistic perspective, 2012. ,
Efficient discovery of overlapping communities in massive networks, Proceedings of the National Academy of Sciences, vol.110, issue.36, pp.110-14534 ,
DOI : 10.1073/pnas.1221839110
Controversies in the Foundations of Statistics, The American Mathematical Monthly, vol.85, issue.4, pp.231-246, 1978. ,
DOI : 10.2307/2321163
Classical Statistics and Statistical Learning in Imaging Neuroscience. arXiv preprint, 2016. ,
Choosing prediction over explanation in psychology: Lessons from machine learning, 2016. ,
The Elements of Statistical Learning, 2001. ,
Why significant variables aren???t automatically good predictors, Proceedings of the National Academy of Sciences, vol.112, issue.45, pp.13892-13899 ,
DOI : 10.1073/pnas.1518285112
Genome-wide association analysis by lasso penalized logistic regression, Bioinformatics, vol.25, issue.6, pp.714-721, 2009. ,
DOI : 10.1093/bioinformatics/btp041
A few useful things to know about machine learning, Communications of the ACM, vol.55, issue.10, pp.55-78 ,
DOI : 10.1145/2347736.2347755
Bayesian decoding of brain images, NeuroImage, vol.39, issue.1, pp.181-205, 2008. ,
DOI : 10.1016/j.neuroimage.2007.08.013
On the interpretation of weight vectors of linear models in multivariate neuroimaging, NeuroImage, vol.87, pp.96-110, 2014. ,
DOI : 10.1016/j.neuroimage.2013.10.067
Statistical learning and selective inference, Proceedings of the National Academy of Sciences, vol.112, issue.25, pp.7629-7663 ,
DOI : 10.1073/pnas.1507583112
Hierarchical Bayesian nonparametric models with applications, Bayesian nonparametrics, issue.1, 2010. ,
DOI : 10.1017/CBO9780511802478.006
Connectivity-based parcellation: Critique and implications, Human Brain Mapping, vol.5, issue.5, 2015. ,
DOI : 10.1002/hbm.22933
URL : https://hal.archives-ouvertes.fr/hal-01184563
The Unreasonable Effectiveness of Mathematics in the Natural Sciences, Communications on Pure and Applied Mathematics, vol.13, pp.1-14, 1960. ,
DOI : 10.1142/9789814503488_0018
An introduction to statistical learning The predictive capacity of personal genome sequencing, Science translational medicine, vol.112, issue.81 4133, pp.133-58, 2012. ,
Neuroscience Training for the 21st Century, Neuron, vol.90, issue.5, pp.917-926, 2006. ,
DOI : 10.1016/j.neuron.2016.05.030
Generative or discriminative? getting the best of both worlds. Bayesian statistics, pp.3-24, 2007. ,
On discriminative vs. generative classifiers: A comparison of logistic regression and naive bayes Advances in neural information processing systems, p.841, 2002. ,
The "wake-sleep" algorithm for unsupervised neural networks, Science, vol.268, issue.5214, pp.1158-1161, 1995. ,
DOI : 10.1126/science.7761831
Bootstrap methods: another look at the jackknife. The annals of Statistics, pp.1-26, 1979. ,
A message from the President: The era of big data, ISBA Bulletin, vol.18, issue.2, pp.1-3, 2011. ,
On the computational complexity of highdimensional Bayesian variable selection, 2015. ,
An Introduction to Variational Methods for Graphical Models, Machine learning, pp.183-233, 1999. ,
DOI : 10.1007/978-94-011-5014-9_5
Information theory, inference and learning algorithms-supervised learning with deep generative models, Advances in Neural Information Processing Systems, 2003. ,
Studies in crop variation. II. The manurial response of different potato varieties, The Journal of Agricultural Science, vol.13, issue.03, pp.13-311, 1923. ,
DOI : 10.1017/S0021859600003592
The ASA's statement on p-values: context, process, and purpose. The American Statistician, pp.0-00 ,
Toward evidence-based medical statistics. 1: The P value fallacy. Annals of internal medicine, pp.995-1004, 1999. ,
Some Difficulties of Interpretation Encountered in the Application of the Chi-Square Test, Journal of the American Statistical Association, vol.33, issue.203, pp.526-536, 1938. ,
DOI : 10.1080/01621459.1938.10502329
Submodel selection and evaluation in regression. The X-random case. International statistical review/revue internationale de Statistique, pp.291-319, 1992. ,
Learning from data, California: AMLBook. 99. Vapnik, V.N., Statistical Learning Theory, 1989. ,
The reusable holdout: Preserving validity in adaptive data analysis, Science, vol.349, issue.6248, pp.636-638, 2015. ,
DOI : 10.1126/science.aaa9375