The mission of the Machine Learning Team is to support researchers in the NIMH intramural research program who want to address research problems in clinical and cognitive neuroscience using machine learning approaches. We do this by consulting with individual researchers and guiding them in the use of the appropriate tools and methods, or by taking on the analysis process ourselves, if this is more expedient. In parallel, we develop new methods and analysis approaches, motivated by the needs of researchers or by the practical possibilities arising from advances in the field.
Team members
The Machine Learning Team supports researchers in the NIMH intramural research program who want to address research problems in clinical and cognitive neuroscience using machine learning approaches.
Team Members
Preprints (under review)
- "Evaluating Adversarial Robustness for Deep Neural Network Interpretability in fMRI Decoding"
McClure, P., Moraczewski, D., Lam, K. C., Thomas, A., Pereira, F. - "Understanding Object Affordances Through Verb Usage Patterns"
Lam, K.C., Pereira, F., Vaziri-Pashkam, M., Woodard, K., McMahon, E.
Selected publications by team members
- "Revealing the multidimensional mental representations of natural objects underlying human similarity judgments"
Hebart, M., Zheng, C., Pereira, F., Baker, C.
to appear in Nature Human Behaviour - "Knowing What You Know in Brain Segmentation Using Bayesian Deep Neural Networks"
McClure, P., Rho, N., Lee, J., Kaczmarzyk, J., Zheng, C., Ghosh, S., Nielson, D., Thomas, A., Bandettini, P., Pereira, F.
Frontiers in Neuroinformatics, 2019 - "Revealing interpretable object representations from human behavior"
Zheng, C., Pereira, F., Baker, C., Hebart, M.
in Proceedings of the International Conference on Learning Representations, 2019 - "Deep Neural Networks in Computational Neuroscience"
Kietzmann, T., McClure, P., Kriegeskorte, N.
Oxford Research Encyclopedia of Neuroscience, 2019 - "Extrapolating Expected Accuracies for Large Multi-Class Problems"
Zheng, C., Achanta R., Benjamini. Y.
Journal of Machine Learning Research vol. 19. 2018. - "Distributed Weight Consolidation: A Brain Segmentation Case Study"
McClure P., Zheng C., Kaczmarzyk J., Rogers-Lee J., Ghosh S., Nielson D., Bandettini P., Pereira F.
in Proceedings of the Neural Information Processing Systems conference, 2018 - "Toward a universal decoder of linguistic meaning from brain activation"
Pereira F., Lou B., Pritchett B., Ritter S., Gershman S., Kanwisher N., Botvinick M., Fedorenko E.
Nature Communications 9 (963), 2018 - "Recurrent Convolutional Neural Networks: A Better Model of Biological Object Recognition"
Spoerer C., McClure P., Kriegeskorte N.
Frontiers in Psychology, 8, 1551, 2017 - "A comparative evaluation of off-the-shelf distributed semantic representations for modelling behavioural data"
Pereira F., Gershman S., Ritter S., Botvinick M.
Cognitive Neuropsychology 33.3-4: 175-190, 2016 - "Representational distance learning for deep neural networks"
McClure P., Kriegeskorte N.
Frontiers in Computational Neuroscience, 10, 131, 2016 - "Using Wikipedia to produce semantic feature representations of concrete concepts in neuroimaging experiments"
Pereira F., Detre G., Botvinick M.
Artificial Intelligence Journal, 194: 240-252 (2013) - "A systematic approach to extracting semantic information from functional MRI data"
Pereira F., Botvinick M.
in Proceedings of the Neural Information Processing Systems conference, 2012 - "Early assessment of malignant lung nodules based on the spatial analysis of detected lung nodules"
El-Baz A., Soliman A., McClure P., Gimel'farb G., Abo El-Ghar M., Falk R.
in IEEE International Symposium on Biomedical Imaging (ISBI), 2012 - "Generating text from functional brain images"
Pereira F., Detre G., Botvinick M.
Frontiers in Human Neuroscience 5:72, 2011 - "Reproducibility Distinguishes Conscious from Nonconscious Neural Representations"
Schurger A., Pereira F., Treisman A., Cohen J.D.
Science, 327: 97-99, 2010 - "Machine learning classifiers and fMRI: a tutorial overview"
Pereira F., Mitchell T., Botvinick M..
NeuroImage, Volume 45, Issue 1, Supplement 1, Pages S199-S209, 2009 - "The Support Vector Decomposition Machine"
Pereira F., Gordon G.
in Proceedings of International Conference on Machine Learning, 2006