Singer, J. J. D.; Cichy, R. M.; Hebart, M. N.: The spatiotemporal neural dynamics of object recognition for natural images and line drawings. The Journal of Neuroscience 43 (3), S. 484 - 500 (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)

Kaniuth, P.; Hebart, M. N.: Feature-reweighted representational similarity analysis: A method for improving the fit between computational models, brains, and behavior. NeuroImage 257, 119294 (2022)

Singer, J.; Seeliger, K.; Kietzmann, T. C.; Hebart, M. N.: From photos to sketches-how humans and deep neural networks process objects across different levels of visual abstraction. Journal of Vision 22 (2), 4 (2022)

Grootswagers, T.; Zhou, I.; Robinson, A. K.; Hebart, M. N.; Carlson, T. A.: Human EEG recordings for 1,854 concepts presented in rapid serial visual presentation streams. Scientific Data 9 (1), 3 (2022)

Muttenthaler, L.; Hebart, M. N.: THINGSvision: A Python toolbox for streamlining the extraction of activations from deep neural networks. Frontiers in Neuroinformatics 15, 679838 (2021)

Liu, P.; Chrysidou, A.; Doehler, J.; Hebart, M. N.; Wolbers, T.; Kuehn, E.: The organizational principles of de-differentiated topographic maps in somatosensory cortex. eLife 10, e60090 (2021)

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)

Hebart, M. N.; Zheng, C. Y.; Pereira, F.; Baker, C. I.: Revealing the multidimensional mental representations of natural objects underlying human similarity judgements. Nature Human Behaviour 4 (11), S. 1173 - 1185 (2020)

Hebart, M. N.; Schuck, N. W.: Current topics in computational cognitive neuroscience. Neuropsychologia 147, 107621 (2020)

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, Bd. 37. (2024)

Hebart, M. N.: Revealing interpretable object representations from human visual cortex and artificial neural networks. In: Proceedings of the 11th International Winter Conference on Brain-Computer Interface (BCI). IEEE (2023)

Hansen, H.; Hebart, M. N.: Semantic features of object concepts generated with GPT-3. In: Proceedings of the Annual Meeting of the Cognitive Science Society, Bd. 44. (2022)

Singer, J.; Karapetian, A.; Hebart, M. N.; Cichy, R.: Revealing the locus and content of behaviorally relevant information about real-world scenes in human visual cortex. In Journal of Vision, 23, 4712. Scholar One, Inc., Charlottesville, VA (2023)

Schmidt, F.; Hebart, M. N.; Schmid, A.; Fleming, R. W.: The mental representation of materials distilled from >1.5 million similarity judgements. In Journal of Vision, 21, 1981. Scholar One, Inc., Charlottesville, VA (2021)

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)

Kaniuth, P.; Hebart, M. N.: Tuned representational similarity analysis: Improving the fit between computational models of vision and brain data. In Journal of Vision, 20, 1076. Vision Sciences Society Annual Meeting 2020, 19. Juni 2020 - 24. Juni 2020. Scholar One, Inc., Charlottesville, VA (2020)

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)