Publications of Katja Seeliger

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

Golan, T.; Taylor, J.; Schütt, H.; Peters, B.; Sommers, R. P.; Seeliger, K.; Doerig, A.; Linton, P.; Konkle, T.; van Gerven, M. et al.; Kording, K.; Richards, B.; Kietzmann, T. C.; Lindsay, G. W.; Kriegeskorte, N.: Deep neural networks are not a single hypothesis but a language for expressing computational hypotheses. Behavioral and Brain Sciences 46, e392 (2023)

Doerig, A.; Sommers, R. P.; Seeliger, K.; Richards, B.; Ismael, J.; Lindsay, G. W.; Kording, K. P.; Konkle, T.; van Gerven, M. A. J.; Kriegeskorte, N. et al.; Kietzmann, T. C.: The neuroconnectionist research programme. Nature Reviews Neuroscience 24 (7), pp. 431 - 450 (2023)

Le, L.; Ambrogioni, L.; Seeliger, K.; Güçlütürk, Y.; van Gerven, M.; Güçlü, U.: Brain2Pix: Fully convolutional naturalistic video frame reconstruction from brain activity. Frontiers in Neuroscience 16, 940972 (2022)

Seeliger, K.; Ambrogioni, L.; Güçlütürk, Y.; van den Bulk, L. M.; Güçlü, U.; van Gerven, M. A. J.: End-to-end neural system identification with neural information flow. PLoS Computational Biology 17 (2), e1008558 (2021)

Le, L.; Papale, P.; Seeliger, K.; Lozano, A.; Dado, T.; Wang, F.; Roelfsema, P.; van Gerven, M.; Güçlütürk, Y.; Güçlü, U.: MonkeySee: Space-time-resolved reconstructions of natural images from macaque multi-unit activity. In: Advances in Neural Information Processing Systems, Vol. 37. (2024)

Seeliger, K.; Roth, J.; Schmid, T.; Hebart, M. N.: Synthesizing preferred stimuli for individual voxels in the human visual system. In Journal of Vision, 21, 2311. Scholar One, Inc., Charlottesville, VA (2021)

Seeliger, K.: Leveraging massive fMRI data sets and deep learning to synthesize images preferred by higher visual system areas. Neural Coding Lab (Umut Güçlü), Donders Institute, Radboud University, Nijmegen, the Netherlands (virtual) (2023)

Seeliger, K.: Leveraging massive fMRI data sets and deep learning to synthesize images preferred by higher visual system areas. Bradley Love Lab, Division of Psychology and Language Sciences, University College London, United Kingdom (2023)

Seeliger, K.: Leveraging massive fMRI data sets and deep learning to synthesize images preferred by higher visual system areas. Retreat of Neural Dynamics of Visual Cognition Lab (Radoslaw Cichy, FU Berlin), Eberswalde, Germany (2023)

Seeliger, K.: Leveraging massive fMRI data sets and deep learning to synthesize images preferred by higher visual system areas. Roelfsema Group, Netherlands Institute for Neuroscience, Amsterdam, the Netherlands (2023)

Seeliger, K.: Convolutional neural networks and visual information processing. Osnabrück Search Symposium Computational Neuroscience , Virtual (2021)

Seeliger, K.: A large single-participant fMRI dataset for probing brain responses to naturalistic stimuli in space and time. Symposium on Naturalistic Stimuli in Cognitive Neuroscience, Virtual (2021)

Seeliger, K.; Leipe, R.; Roth, J.; Hebart, M. N.: Uncovering high-level visual cortex preferences by training convolutional neural networks on large neuroimaging data. Neuro-AI-Talks (NEAT), Osnabrück, Germany (2023)

Seeliger, K.; Leipe, R.; Roth, J.; Hebart, M. N.: Investigating high-level visual cortex preferences through neural network training on large neuroimaging data. Salzburg Mind Brain Meeting (SAMBA), Salzburg, Austria (2023)

Seeliger, K.; Leipe, R.; Roth, J.; Hebart, M. N.: Uncovering high-level visual cortex preferences by training convolutional neural networks on large neuroimaging data. 23rd Annual Meeting of the Vision Science Society (VSS), St. Pete Beach, FL, USA (2023)

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. 23rd Annual Meeting of the Vision Science Society (VSS), St. Pete Beach, FL, USA (2023)

St-Laurent, M.; Pinsard, B.; Contier, O.; Seeliger, K.; Borghesani, V.; Boyle, J.; Bellec, P.; 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. 23rd Annual Meeting of the Vision Science Society (VSS), St. Pete Beach, FL, USA (2023)

Mahner, F.; Seeliger, K.; Umut, G.; Hebart, M. N.: Learning cortical magnification with brain-optimized convolutional neural networks. Conference on Cognitive Computational Neuroscience, San Francisco, CA, USA (2022)

Seeliger, K.: Synthesizing preferred stimuli for individual voxels in the human visual system. Vision Sciences Society (V-VSS), Virtual (2021)