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Tacchetti, A., Isik, L. & Poggio, T. Invariant Recognition Shapes Neural Representations of Visual Input. Annual Review of Vision Science 4, 403 - 422 (2018).PDF icon annurev-vision-091517-034103.pdf (1.55 MB)
Tacchetti, A., Isik, L. & Poggio, T. Invariant recognition drives neural representations of action sequences. PLOS Computational Biology 13, e1005859 (2017).PDF icon journal.pcbi_.1005859.pdf (9.24 MB)
Tacchetti, A., Voinea, S. & Evangelopoulos, G. Discriminate-and-Rectify Encoders: Learning from Image Transformation Sets. (2017).PDF icon CBMM-Memo-062.pdf (9.37 MB)
Tacchetti, A., Isik, L. & Poggio, T. Invariant recognition drives neural representations of action sequences. PLoS Comp. Bio (2017).
Tacchetti, A., Voinea, S., Evangelopoulos, G. & Poggio, T. Representation Learning from Orbit Sets for One-shot Classification. AAAI Spring Symposium Series, Science of Intelligence (2017). at <https://www.aaai.org/ocs/index.php/SSS/SSS17/paper/view/15357>
Tan, C. & Poggio, T. Neural tuning size is a key factor underlying holistic face processing. (2014).PDF icon CBMM-Memo-021-1406.3793.pdf (387.79 KB)
Tan, C. & Poggio, T. Neural Tuning Size in a Model of Primate Visual Processing Accounts for Three Key Markers of Holistic Face Processing. Public Library of Science | PLoS ONE 1(3): e0150980, (2016).PDF icon journal.pone_.0150980.PDF (384.15 KB)
Tang, H. et al. Spatiotemporal Dynamics Underlying Object Completion in Human Ventral Visual Cortex. Neuron 83, 736 - 748 (2014).
Tang, H. et al. Cascade of neural processing orchestrates cognitive control in human frontal cortex. eLIFE (2016). doi:10.7554/eLife.12352PDF icon Manuscript  (1.83 MB)
Tang, H. et al. Predicting episodic memory formation for movie events [dataset]. (2016).
Tang, H. et al. Cascade of neural processing orchestrates cognitive control in human frontal cortex [dataset]. (2016). at <http://klab.tch.harvard.edu/resources/tangetal_stroop_2016.html>
Tang, H. et al. Recurrent computations for visual pattern completion. Proceedings of the National Academy of Sciences (2018). doi:10.1073/pnas.1719397115PDF icon 1719397115.full_.pdf (1.1 MB)
Tang, H. et al. Predicting episodic memory formation for movie events [code]. (2016).
Tang, H. et al. Predicting episodic memory formation for movie events. Scientific Reports (2016). doi:10.1038/srep30175
Tang, H. et al. Cascade of neural processing orchestrates cognitive control in human frontal cortex [code]. (2016). at <http://klab.tch.harvard.edu/resources/tangetal_stroop_2016.html>
Tang, H. et al. A machine learning approach to predict episodic memory formation. 2016 Annual Conference on Information Science and Systems (CISS) 539 - 544 (2016). doi:10.1109/CISS.2016.7460560
Tang, H., Kreiman, G. & Zhao, Q. Computational and Cognitive Neuroscience of Vision (Springer Singapore, 2017). at <http://www.springer.com/us/book/9789811002113>
Tang, H., Buia, C., Madsen, J., Anderson, W. S. & Kreiman, G. A role for recurrent processing in object completion: neurophysiological, psychophysical and computational evidence. (2014).PDF icon CBMM-Memo-009.pdf (4.21 MB)
Tazi, Y., Berger, M. & Freiwald, W. A. Towards an objective characterization of an individual's facial movements using Self-Supervised Person-Specific-Models. arXiv (2022). at <https://arxiv.org/abs/2211.08279>
Tegmark, M. Improved Measures of Integrated Information. PLOS Computational Biology (2016). doi:10.1371/journal.pcbi.100512310.1371PDF icon 1601.02626.pdf (3.49 MB)
Tejwani, R. et al. Incorporating Rich Social Interactions Into MDPs. (2022).PDF icon CBMM-Memo-133.pdf (1.68 MB)
Tejwani, R. et al. Incorporating Rich Social Interactions Into MDPs. 2022 IEEE International Conference on Robotics and Automation (ICRA)2022 International Conference on Robotics and Automation (ICRA) (2022). doi:10.1109/ICRA46639.2022.9811991
Tejwani, R., Kuo, Y. - L., Shu, T., Katz, B. & Barbu, A. Social Interactions as Recursive MDPs. (2021).PDF icon CBMM-Memo-130.pdf (1.52 MB)
Tejwani, R. et al. Zero-shot linear combinations of grounded social interactions with Linear Social MDPs. Proceedings of the 37th AAAI Conference on Artificial Intelligence (AAAI) (2023).
Telenczuk, B. et al. Local field potentials primarily reflect inhibitory neuron activity in human and monkey cortex. Nature Scientific Reports (2017). doi:10.1038/srep40211PDF icon srep40211.pdf (2.53 MB)

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