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Nikolaus Kriegeskorte
Nikolaus Kriegeskorte
Professor of Psychology and Neuroscience, Columbia University
Verified email at columbia.edu
Title
Cited by
Year
" Recurrent convolutional neural networks: A better model of biological object recognition": Corrigendum.
CJ Spoerer, P McClure, N Kriegeskorte
Frontiers Media SA, 2018
2018
" Retrieval induces adaptive forgetting of competing memories via cortical pattern suppression": Correction.
M Wimber, A Alink, I Charest, N Kriegeskorte, MC Anderson
Nature Publishing Group, 2018
2018
4AFC (four alternative forced choice) task, 296–297 Accuracy of model predictions, 163, 168–170, 180 representational, 421, 427, 436
AIT See
Blood 542, 549, 2016
42016
A deep learning framework for neuroscience
BA Richards, TP Lillicrap, P Beaudoin, Y Bengio, R Bogacz, ...
Nature neuroscience 22 (11), 1761-1770, 2019
8502019
A head view-invariant representation of gaze direction in anterior superior temporal sulcus
JD Carlin, AJ Calder, N Kriegeskorte, H Nili, JB Rowe
Current Biology 21 (21), 1817-1821, 2011
1322011
A multimodal approach to representational similarity analysis
L Su, C Wingfield, M Bozic, E Fonteneau, WD Marslen-Wilson, ...
Poster presented at 16th Annual Meeting of the Organization for Human Brain …, 2010
12010
A neural network family for systematic analysis of RF size and computational-path-length distribution as determinants of neural predictivity and behavioral performance
B Peters, L Stoffl, N Kriegeskorte
Journal of Vision 22 (14), 4287-4287, 2022
12022
A toolbox for representational similarity analysis
H Nili, C Wingfield, A Walther, L Su, W Marslen-Wilson, N Kriegeskorte
PLoS computational biology 10 (4), e1003553, 2014
8472014
Abstract encoding of auditory objects in cortical activity patterns
BL Giordano, S McAdams, RJ Zatorre, N Kriegeskorte, P Belin
Cerebral cortex 23 (9), 2025-2037, 2013
1022013
Adaptive geo-topological independence criterion
B Lin, N Kriegeskorte
arXiv preprint arXiv:1810.02923, 2018
52018
Adaptive independence tests with geo-topological transformation
B Lin, N Kriegeskorte
arXiv preprint arXiv:1810.02923, 2018
12018
Adjudicating between face-coding models with individual-face fMRI responses
JD Carlin, N Kriegeskorte
PLoS computational biology 13 (7), e1005604, 2017
502017
Affinity-based attention in self-supervised transformers predicts dynamics of object grouping in humans
H Adeli, S Ahn, N Kriegeskorte, G Zelinsky
arXiv preprint arXiv:2306.00294, 2023
32023
An ecologically motivated image dataset for deep learning yields better models of human vision
J Mehrer, CJ Spoerer, EC Jones, N Kriegeskorte, TC Kietzmann
Proceedings of the National Academy of Sciences 118 (8), e2011417118, 2021
1142021
An efficient algorithm for topologically correct segmentation of the cortical sheet in anatomical MR volumes
N Kriegeskorte, R Goebel
Neuroimage 14 (2), 329-346, 2001
2762001
An emerging consensus for open evaluation: 18 visions for the future of scientific publishing
N Kriegeskorte, A Walther, D Deca
Frontiers in computational neuroscience 6, 94, 2012
572012
Analyses of the neural population dynamics during human object vision reveal two types of representational echoes that reverberate across the visual system.
P Sulewski, P Koenig, N Kriegeskorte, TC Kietzmann
PERCEPTION, 2022
2022
Analysing linear multivariate pattern transformations in neuroimaging data
A Basti, M Mur, N Kriegeskorte, V Pizzella, L Marzetti, O Hauk
PLoS one 14 (10), e0223660, 2019
222019
Analyzing disentanglement of visual objects in semi-supervised neural networks
AD Zaharia, B Peters, J Cunningham, N Kriegeskorte
Analyzing for information, not activation, to exploit high-resolution fMRI
N Kriegeskorte, P Bandettini
Neuroimage 38 (4), 649-662, 2007
3422007
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