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 | 182 | 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 | 131 | 2018 |
The neuroconnectionist research programme A Doerig, RP Sommers, K Seeliger, B Richards, J Ismael, GW Lindsay, ... Nature Reviews Neuroscience, 1-20, 2023 | 90 | 2023 |
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 | 88* | 2017 |
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 | 42 | 2021 |
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 | 39 | 2011 |
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, ... | 24 | 2018 |
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 | 23* | 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 | 17 | 2019 |
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 | 13 | 2019 |
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 | 4 | 2023 |
Modeling cognitive processes with neural reinforcement learning SE Bosch, K Seeliger, MAJ van Gerven bioRxiv, 084111, 2016 | 4 | 2016 |
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 |
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 |