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Katja Seeliger
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Generative adversarial networks for reconstructing natural images from brain activity
K Seeliger, U Güçlü, L Ambrogioni, Y Güçlütürk, MAJ van Gerven
NeuroImage 181, 775-785, 2018
1702018
Convolutional neural network-based encoding and decoding of visual object recognition in space and time
K Seeliger, M Fritsche, U Güçlü, S Schoenmakers, JM Schoffelen, ...
NeuroImage 180, 253-266, 2018
1252018
Reconstructing perceived faces from brain activations with deep adversarial neural decoding
Y Güçlütürk, U Güçlü, K Seeliger, SE Bosch, R van Lier, MAJ van Gerven
Advances in Neural Information Processing Systems, 4246-4257, 2017
85*2017
The neuroconnectionist research programme
A Doerig, RP Sommers, K Seeliger, B Richards, J Ismael, GW Lindsay, ...
Nature Reviews Neuroscience, 1-20, 2023
742023
End-to-end neural system identification with neural information flow
K Seeliger, L Ambrogioni, Y Güçlütürk, LM van den Bulk, U Güçlü, ...
PLOS Computational Biology 17 (2), e1008558, 2021
412021
Simulation data mining for supporting bridge design
S Burrows, B Stein, J Frochte, D Wiesner, K Seeliger
Proceedings of the Ninth Australasian Data Mining Conference-Volume 121, 163-170, 2011
362011
From photos to sketches - how humans and deep neural networks process objects across different levels of visual abstraction
JJD Singer, K Seeliger, TC Kietzmann, MN Hebart
Journal of Vision 22 (4), 2022
26*2022
You Only Look on Lymphocytes Once
M van Rijthoven, Z Swiderska-Chadaj, K Seeliger, J van der Laak, ...
242018
Brain2Pix: Fully convolutional naturalistic video frame reconstruction from brain activity
L Le, L Ambrogioni, K Seeliger, Y Güçlütürk, M Van Gerven, U Güçlü
Frontiers in Neuroscience, 1684, 2022
18*2022
A large single-participant fMRI dataset for probing brain responses to naturalistic stimuli in space and time
K Seeliger, RP Sommers, U Güçlü, SE Bosch, MAJ van Gerven
bioRxiv, 687681, 2019
162019
Current Advances in Neural Decoding
MAJ van Gerven, K Seeliger, U Güçlü, Y Güçlütürk
Explainable AI: Interpreting, Explaining and Visualizing Deep Learning, 379-394, 2019
122019
Modeling cognitive processes with neural reinforcement learning
SE Bosch, K Seeliger, MAJ van Gerven
bioRxiv, 084111, 2016
42016
Deep neural networks are not a single hypothesis but a language for expressing computational hypotheses
T Golan, JM Taylor, H Schütt, B Peters, RP Sommers, K Seeliger, ...
Behavioral and Brain Sciences 46, 2023
22023
Synthesizing preferred stimuli for individual voxels in the human visual system
K Seeliger, J Roth, T Schmid, M Hebart
Journal of Vision 21 (9), 2311-2311, 2021
12021
What comparing deep neural networks can teach us about human vision
K Seeliger, MN Hebart
Nature Machine Intelligence, 1-2, 2024
2024
cneuromod-things: a large-scale fMRI dataset for task-and data-driven assessment of object representation and visual memory recognition in the human brain
M St-Laurent, B Pinsard, O Contier, K Seeliger, V Borghesani, J Boyle, ...
Journal of Vision 23 (9), 5424-5424, 2023
2023
Revealing interpretable object dimensions from a high-throughput model of the fusiform face area
O Contier, S Fujimori, K Seeliger, NAR Murty, M Hebart
Journal of Vision 23 (9), 5356-5356, 2023
2023
Uncovering high-level visual cortex preferences by training convolutional neural networks on large neuroimaging data
K Seeliger, R Leipe, J Roth, MN Hebart
Journal of Vision 23 (9), 5493-5493, 2023
2023
Investigating high-level visual cortex preferences through neural network training on large neuroimaging data
K Seeliger, R Leipe, J Roth, MN Hebart
Salzburg Mind Brain Meeting (SAMBA), 2023
2023
Leveraging massive fMRI data sets and deep learning to synthesize images preferred by higher visual system areas
K Seeliger
Roelfsema Group, Netherlands Institute for Neuroscience, 2023
2023
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