Nikolaus Kriegeskorte
Affiliations: | National Institute of Mental Health, Bethesda, MD, United States |
Area:
object vision, IT, pattern-information analysisWebsite:
http://fim.nimh.nih.gov/people/NKGoogle:
"Nikolaus Kriegeskorte"Mean distance: 13.51 (cluster 29) | S | N | B | C | P |
Children
Sign in to add trainee| Prashant C. Raju | research assistant | 2017-2020 | Columbia |
| Aneesh Kashalikar | research assistant | 2019-2021 | Columbia |
| Baihan Lin | grad student | 2017- | Columbia |
| Heiko H Schütt | grad student | 2018- | Columbia |
| Seyed-Mahdi Khaligh-Razavi | grad student | 2012-2014 | Cambridge |
| Tal Golan | post-doc | ||
| Benjamin Peters | post-doc | ||
| Ruben S. van Bergen | post-doc | 2019- | Columbia |
| Vassilis Pelekanos | post-doc | 2015-2017 | MRC CBU Cambridge |
| Robert M. Mok | post-doc | 2016-2017 | |
| Tim Christian Kietzmann | post-doc | 2016-2019 | Cambridge |
| Olivier Joly | research scientist | 2013-2015 | MRC-CBU |
BETA: Related publications
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Publications
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| Peters B, Kriegeskorte N. (2021) Capturing the objects of vision with neural networks. Nature Human Behaviour. 5: 1127-1144 |
| Storrs KR, Kietzmann TC, Walther A, et al. (2021) Diverse Deep Neural Networks All Predict Human IT Well, After Training and Fitting. Journal of Cognitive Neuroscience. 1-21 |
| Ainsworth M, Sallet J, Joly O, et al. (2021) Viewing ambiguous social interactions increases functional connectivity between frontal and temporal nodes of the social brain. The Journal of Neuroscience : the Official Journal of the Society For Neuroscience |
| Mehrer J, Spoerer CJ, Jones EC, et al. (2021) An ecologically motivated image dataset for deep learning yields better models of human vision. Proceedings of the National Academy of Sciences of the United States of America. 118 |
| van Bergen RS, Kriegeskorte N. (2020) Going in circles is the way forward: the role of recurrence in visual inference. Current Opinion in Neurobiology. 65: 176-193 |
| Golan T, Raju PC, Kriegeskorte N. (2020) Controversial stimuli: Pitting neural networks against each other as models of human cognition. Proceedings of the National Academy of Sciences of the United States of America. 117: 29330-29337 |
| Mehrer J, Spoerer CJ, Kriegeskorte N, et al. (2020) Individual differences among deep neural network models. Nature Communications. 11: 5725 |
| Spoerer CJ, Kietzmann TC, Mehrer J, et al. (2020) Recurrent neural networks can explain flexible trading of speed and accuracy in biological vision. Plos Computational Biology. 16: e1008215 |
| Pelekanos V, Mok RM, Joly O, et al. (2020) Rapid event-related, BOLD fMRI, non-human primates (NHP): choose two out of three. Scientific Reports. 10: 7485 |
| Richards BA, Lillicrap TP, Beaudoin P, et al. (2019) A deep learning framework for neuroscience. Nature Neuroscience. 22: 1761-1770 |