| 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 | 89 | 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 | 83* | 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 | 56* | 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 | 29 | 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 | 17 | 2021 |
| You Only Look on Lymphocytes Once M van Rijthoven, Z Swiderska-Chadaj, K Seeliger, J van der Laak, ... | 11 | 2018 |
| 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 | 6 | 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 | 5 | 2019 |
| Brain2Pix: Fully convolutional naturalistic video reconstruction from brain activity L Le, L Ambrogioni, K Seeliger, Y Güçlütürk, M van Gerven, U Güçlü | 4 | 2021 |
| Modeling cognitive processes with neural reinforcement learning SE Bosch, K Seeliger, MAJ van Gerven bioRxiv, 084111, 2016 | 3 | 2016 |
| The representation of object drawings and sketches in deep convolutional neural networks J Singer, K Seeliger, MN Hebart NeurIPS workshop on Shared Visual Representations in Human & Machine …, 2020 | 2 | 2020 |
| From photos to sketches - how humans and deep neural networks process objects across different levels of visual abstraction J Singer, K Seeliger, TC Kietzmann, MN Hebart PsyArXiv, 2021 | 1 | 2021 |
| Neural information flow: Learning neural information processing systems from brain activity K Seeliger, L Ambrogioni, U Güçlü, MAJ van Gerven 2019 Conference on Cognitive Computational Neuroscience, 2019 | 1 | 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 | | 2021 |
| Convolutional neural networks in vision neuroscience K Seeliger [Sl: sn], 2020 | | 2020 |
| A Large Single-Participant Functional Magnetic Resonance Imaging Data Set for Probing Brain Responses to Naturalistic Stimuli in Space and Time K Seeliger, RP Sommers, U Guclu, SE Bosch, MAJ van Gerven PERCEPTION 48, 5-6, 2019 | | 2019 |
| Generalization of an upper bound on the number of nodes needed to achieve linear separability M Troost, K Seeliger, MAJ van Gerven arXiv preprint arXiv:1802.03488, 2018 | | 2018 |
| Deep Learning with Symbols Hackathon DL Silver, JC Davidson, D Hupkes, I Noble, K Seeliger Human-Like Neural-Symbolic Computing 2 (10), 80, 2017 | | 2017 |
| Detecting spatial auditory attention in cocktail-party situations M Tangermann, K Mueller, A Nolte, J Schumacher, P Zhutovsky, ... Clinical Neurophysiology, S147, 2014 | | 2014 |
Marcel van GervenProfessor of Artificial Cognitive Systems, Donders Institute for Brain, Cognition and BehaviourVerified email at donders.ru.nl
