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.
Nicole Kuznetsov
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.
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] - "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 - "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 - "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
Yenho Chen
