| 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 | 120 | 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 | 103 | 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 | 74* | 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 | 33 | 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 | 29 | 2021 |
| You Only Look on Lymphocytes Once M van Rijthoven, Z Swiderska-Chadaj, K Seeliger, J van der Laak, ... | 16 | 2018 |
| 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 | 13* | 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 | 11 | 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 | 9* | 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 | 8 | 2019 |
| The neuroconnectionist research programme A Doerig, R Sommers, K Seeliger, B Richards, J Ismael, G Lindsay, ... arXiv preprint arXiv:2209.03718, 2022 | 4 | 2022 |
| Modeling cognitive processes with neural reinforcement learning SE Bosch, K Seeliger, MAJ van Gerven bioRxiv, 084111, 2016 | 3 | 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 | 2 | 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 | 1 | 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 | | 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 | | 2022 |
| Training BigGAN on an ecologically motivated image dataset W Kłos, P Coronica, K Seeliger, MN Hebart Conference on Cognitive Computational Neuroscience, 2022 | | 2022 |
| Learning Cortical Magnification with Brain-Optimized Convolutional Neural Networks FP Mahner, K Seeliger, U Güçlü, MN Hebart Conference on Cognitive Computational Neuroscience, 2022 | | 2022 |
| Convolutional neural networks and visual information processing K Seeliger Osnabrück Search Symposium Computational Neuroscience, 2021 | | 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 | | 2021 |
Marcel van GervenProfessor of Artificial Cognitive Systems, Donders Institute for Brain, Cognition and BehaviourVerified email at donders.ru.nl
