Nikolaus Kriegeskorte

National Institute of Mental Health, Bethesda, MD, United States 
object vision, IT, pattern-information analysis
"Nikolaus Kriegeskorte"
Mean distance: 13.51 (cluster 29)


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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


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Roozbeh Kiani collaborator 2006-2009 Columbia
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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
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