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
Dylan Nielson
Job title
research scientist
Nicole Kuznetsov
Job title
postbac IRTA
Preprints (under review)
- "Improving the Interpretability of fMRI Decoding using Deep Neural Networks and Adversarial Robustness"
- McClure P., Moraczewski D., Lam K. C., Thomas A., Pereira F.
- "VICE: Variational Interpretable Concept Embeddings"
- Muttenthaler L., Zheng C., McClure P., Vandermeulen R., Hebart M., Pereira F.
Selected publications by team members
- Semantic Projection: Recovering Human Knowledge of Multiple, Distinct, Object Features from Word Embeddings
Grand G., Blank I., Pereira F., Fedorenko E.
to appear in Nature Human Behaviour, 2021 - "The temporal representation of experience in subjective mood"
Keren H., Zheng C., Jangraw D. C., Chang K., Vitale A., Nielson D., Rutledge R. B., Pereira F., Stringaris A.
eLife 2021 - "Mental representations of objects reflect the ways in which we interact with them"
Lam K. C., Pereira F., Vaziri-Pashkam M., Woodard K., McMahon E.
Proceedings of the Cognitive Science Society Conference, 2021 [talk] - "Magnetoencephalographic Correlates of Mood and Reward Dynamics in Human Adolescents"
Liuzzi, L., Chang, K.K., Zheng, C., Keren, H., Saha, D., Nielson, D.M. and Stringaris, A.
Cerebral Cortex, 2021 - "Validating the Representational Space of Deep Reinforcement Learning Models of Behavior with Neural Data"
Bruch S. N. , McClure P., Zhou J., Schoenbaum G., Pereira F.
bioRxiv preprint - "Revealing the multidimensional mental representations of natural objects underlying human similarity judgments"
Hebart, M., Zheng, C., Pereira, F., Baker, C.
Nature Human Behaviour, 2020 - "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.
International Conference on Learning Representations, 2019 - "Deep Neural Networks in Computational Neuroscience"
Kietzmann, T., McClure, P., Kriegeskorte, N.
Oxford Research Encyclopedia of Neuroscience, 2019 - "Subtle predictive movements reveal actions regardless of social context"
McMahon, E.G., Zheng, C.Y., Pereira, F., Gonzalez, R., Ungerleider, L.G. and Vaziri-Pashkam, M.
Journal of vision 19 (7), 16-16, 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.
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
Former Team Members
Patrick McClure
Job title
Assistant Professor, Computer Science Dept., Naval Postgraduate School
Links
Yenho Chen
Job title
Machine Learning Ph.D. Student
