Katja Seeliger
Geciteerd door
Geciteerd door
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
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
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
Simulation data mining for supporting bridge design
S Burrows, B Stein, J Frochte, D Wiesner, K Müller
Proceedings of the Ninth Australasian Data Mining Conference-Volume 121, 163-170, 2011
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
You Only Look on Lymphocytes Once
M van Rijthoven, Z Swiderska-Chadaj, K Seeliger, J van der Laak, ...
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
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
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
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
The neuroconnectionist research programme
A Doerig, R Sommers, K Seeliger, B Richards, J Ismael, G Lindsay, ...
arXiv preprint arXiv:2209.03718, 2022
Modeling cognitive processes with neural reinforcement learning
SE Bosch, K Seeliger, MAJ van Gerven
bioRxiv, 084111, 2016
Neural information flow: Learning neural information processing systems from brain activity
K Seeliger, L Ambrogioni, U Güçlü, M van Gerven
Conference on Cognitive Computational Neuroscience, 2019
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
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, ...
PsyArXiv, 2023
The role of gaze position in training visual brain encoders on free-viewing data
M St-Laurent, K Seeliger, M Hebart
Journal of Vision 22 (14), 4091-4091, 2022
Training BigGAN on an ecologically motivated image dataset
W Kłos, P Coronica, K Seeliger, MN Hebart
Conference on Cognitive Computational Neuroscience, 2022
Learning Cortical Magnification with Brain-Optimized Convolutional Neural Networks
FP Mahner, K Seeliger, U Güçlü, MN Hebart
Conference on Cognitive Computational Neuroscience, 2022
Convolutional neural networks and visual information processing
K Seeliger
Osnabrück Search Symposium Computational Neuroscience, 2021
Preferred stimuli for individual voxels in the human visual system
J Roth, K Seeliger, T Schmid, MN Hebart
2021 Computational and Systems Neuroscience (Cosyne), 2021
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