Publications of Katja Seeliger

Seeliger, K., & Hebart, M. N. (2024). What comparing deep neural networks can teach us about human vision. Nature Machine Intelligence, 6, 122–123.

Golan, T., Taylor, J. M., Schütt, H., Peters, B., Sommers, R. P., Seeliger, K., … Kriegeskorte, N. (2023). Deep neural networks are not a single hypothesis but a language for expressing computational hypotheses. Behavioral and Brain Sciences, 46. doi:10.1017/S0140525X23001553

Doerig, A., Sommers, R. P., Seeliger, K., Richards, B., Ismael, J., Lindsay, G. W., … Kietzmann, T. C. (2023). The neuroconnectionist research programme. Nature Reviews Neuroscience, 24, 431–450.

Le, L., Ambrogioni, L., Seeliger, K., Güçlütürk, Y., van Gerven, M., & Güçlü, U. (2022). Brain2Pix: Fully convolutional naturalistic video frame reconstruction from brain activity. Frontiers in Neuroscience, 16. doi:10.3389/fnins.2022.940972

Seeliger, K., Ambrogioni, L., Güçlütürk, Y., van den Bulk, L. M., Güçlü, U., & van Gerven, M. A. J. (2021). End-to-end neural system identification with neural information flow. PLoS Computational Biology, 17. doi:10.1371/journal.pcbi.1008558

Le, L., Papale, P., Seeliger, K., Lozano, A., Dado, T., Wang, F., … Güçlü, U. (2024). MonkeySee: Space-time-resolved reconstructions of natural images from macaque multi-unit activity. In Advances in Neural Information Processing Systems (Vol. 37). Retrieved from http://hdl.handle.net/21.11116/0000-0010-FD3B-0

Seeliger, K., Roth, J., Schmid, T., & Hebart, M. N. (2021). Synthesizing preferred stimuli for individual voxels in the human visual system. In Journal of Vision (Vol. 21). Charlottesville, VA: Scholar One, Inc. doi:10.1167/jov.21.9.2311

Seeliger, K. Leveraging massive fMRI data sets and deep learning to synthesize images preferred by higher visual system areas. Talk_at_event presented at the Neural Coding Lab (Umut Güçlü), Donders Institute, Radboud University, Nijmegen, the Netherlands (virtual). Retrieved from http://hdl.handle.net/21.11116/0000-000E-762E-B

Seeliger, K. Leveraging massive fMRI data sets and deep learning to synthesize images preferred by higher visual system areas. Talk_at_event presented at the Bradley Love Lab, Division of Psychology and Language Sciences, University College London, United Kingdom. Retrieved from http://hdl.handle.net/21.11116/0000-000E-7629-0

Seeliger, K. Leveraging massive fMRI data sets and deep learning to synthesize images preferred by higher visual system areas. Talk_at_event presented at the Retreat of Neural Dynamics of Visual Cognition Lab (Radoslaw Cichy, FU Berlin), Eberswalde, Germany. Retrieved from http://hdl.handle.net/21.11116/0000-000E-7627-2

Seeliger, K. Leveraging massive fMRI data sets and deep learning to synthesize images preferred by higher visual system areas. Talk_at_event presented at the Roelfsema Group, Netherlands Institute for Neuroscience, Amsterdam, the Netherlands. Retrieved from http://hdl.handle.net/21.11116/0000-000E-7625-4

Seeliger, K. Convolutional neural networks and visual information processing. Talk_at_event presented at the Osnabrück Search Symposium Computational Neuroscience , Virtual. Retrieved from http://hdl.handle.net/21.11116/0000-000B-2C9E-3

Seeliger, K. A large single-participant fMRI dataset for probing brain responses to naturalistic stimuli in space and time. Talk_at_event presented at the Symposium on Naturalistic Stimuli in Cognitive Neuroscience, Virtual. Retrieved from http://hdl.handle.net/21.11116/0000-000B-2C9C-5

Seeliger, K., Leipe, R., Roth, J., & Hebart, M. N. Uncovering high-level visual cortex preferences by training convolutional neural networks on large neuroimaging data. Poster presented at the Neuro-AI-Talks (NEAT), Osnabrück, Germany. Retrieved from http://hdl.handle.net/21.11116/0000-000E-761C-F

Seeliger, K., Leipe, R., Roth, J., & Hebart, M. N. Investigating high-level visual cortex preferences through neural network training on large neuroimaging data. Poster presented at the Salzburg Mind Brain Meeting (SAMBA), Salzburg, Austria. Retrieved from http://hdl.handle.net/21.11116/0000-000E-7617-4

Seeliger, K., Leipe, R., Roth, J., & Hebart, M. N. Uncovering high-level visual cortex preferences by training convolutional neural networks on large neuroimaging data. Poster presented at the 23rd Annual Meeting of the Vision Science Society (VSS), St. Pete Beach, FL, USA. Retrieved from http://hdl.handle.net/21.11116/0000-000E-75DC-7

Contier, O., Fujimori, S., Seeliger, K., Ratan Murty, N. A., & Hebart, M. N. Revealing interpretable object dimensions from a high-throughput model of the fusiform face area. Poster presented at the 23rd Annual Meeting of the Vision Science Society (VSS), St. Pete Beach, FL, USA. Retrieved from http://hdl.handle.net/21.11116/0000-000E-7604-9

St-Laurent, M., Pinsard, B., Contier, O., Seeliger, K., Borghesani, V., Boyle, J., … Hebart, M. N. Cneuromod-things: A large-scale fMRI dataset for task-and data-driven assessment of object representation and visual memory recognition in the human brain. Poster presented at the 23rd Annual Meeting of the Vision Science Society (VSS), St. Pete Beach, FL, USA. Retrieved from http://hdl.handle.net/21.11116/0000-000E-75DE-5

Mahner, F., Seeliger, K., Umut, G., & Hebart, M. N. Learning cortical magnification with brain-optimized convolutional neural networks. Poster presented at the Conference on Cognitive Computational Neuroscience, San Francisco, CA, USA. Retrieved from http://hdl.handle.net/21.11116/0000-000B-1E5A-0

Seeliger, K. Synthesizing preferred stimuli for individual voxels in the human visual system. Poster presented at the Vision Sciences Society (V-VSS), Virtual. Retrieved from http://hdl.handle.net/21.11116/0000-000B-2CC6-5