Publications

Ventura, L. A., Bosch, V., Kietzmann, T. C., & Thorat, S. (2026). Path Integration and Object-Location Binding Emerge in an Action-Conditioned Predictive Sequence Network. arXiv, arXiv:2602.03490.

Anthes, D., Thorat, S., Mitola, A., Papale, P., König, P., & Kietzmann, T. C. (2026). The illusory simplicity of the feedforward pass: evidence for the dynamical nature of stimulus encoding along the primate ventral stream. arXiv, arXiv:2604.12825.

Bosch, V., Sommers, R., Doerig, A., & Kietzmann, T. C. (2026). The Umwelt Representation Hypothesis: Rethinking Universality. arXiv, arXiv:2604.17960.

Bosch, V., Anthes, D., Doerig, A., Thorat, S., König, P., Kietzmann, T.C. (2025). Brain-language fusion enables interactive neural readout and in-silico experimentation.

Furtak, M., Pätzold, F., Kietzmann, T.C., Kärcher, S.M., & König, P. (2025). Helping Blind People Grasp: Enhancing a Tactile Bracelet with an Automated Hand Navigation System. arXiv, arXiv:2504.16502.

Thorat, S., Doerig, A., Kroner, A., Amme, C., Kietzmann, T.C. (2025). Predicting upcoming visual features during eye movements yields scene representations aligned with human visual cortex.

Sommers, R. P., Thorat, S., Anthes, D., & Kietzmann, T. C. (2025). Sparks of cognitive flexibility: self-guided context inference for flexible stimulus-response mapping by attentional routing. arXiv, arXiv:2502.15634.

Amme, C., Sulewski, P., Spaak, E., Hebart, M. N., König, P., & Kietzmann, T. C. (2024). Saccade onset, not fixation onset, best explains early responses across the human visual cortex during naturalistic vision. bioRxiv, 2024-10.

Lu, Z., Thorat, S., Cichy, R. M., & Kietzmann, T. C. (2026). Adopting a human developmental visual diet yields robust and shape-based AI vision. Nature Machine Intelligence, 8: 735–748.

Sulewski, P., Amme, C., Hebart, M., König, P., & Kietzmann, T.C. (2026). Fixation duration on natural scenes is explained by memory encoding not processing demand. Nature Neuroscience.

Nortmann, T., Sulewski, P., Kietzmann, T.C. (2026). Predictive remapping and allocentric coding as consequences of energy efficiency in recurrent neural network models of active vision. Patterns, 7(1): 101422. DOI: 10.1016/j.patter.2025.101422.

Lu, Z., Doerig, A., Bosch, V., Krahmer, B., Kaiser, D., Cichy, R. M., & Kietzmann, T. C. (2025). End-to-end topographic networks as models of cortical map formation and human visual behaviour. Nature Human Behaviour, 9(9): 1975–1991.

Doerig, A., Kietzmann, T.C., Allen, E., Wu, Y., Naselaris, T., Kay, K., & Charest, I. (2025). High-level visual representations in the human brain are aligned with large language models. Nature Machine Intelligence, 7: 1220–1234.

Richter, D., Kietzmann, T.C., & de Lange, F. P. (2024). High-level visual prediction errors in early visual cortex. PLoS Biology, 22(11), e3002829.

Jozwik, K.M., Kietzmann, T.C., Kriegeskorte, N., Mur, M. (2023). Deep neural networks and visuo-semantic models explain complementary components of human ventral-stream representational dynamics. Journal of Neuroscience, 43(10): 1731–1741.

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

Karapetian, A., Boyanova, A., Pandaram, M., Obermayer, K., Kietzmann, T.C., & Cichy, R. M. (2023). Empirically identifying and computationally modeling the brain-behavior relationship for human scene categorization. Journal of Cognitive Neuroscience, 35(11), 1879-1897.

Doerig, A., Sommers, R., Seeliger, K., Richards, B., Ismael, J., Lindsay, G., Kording, K., Konkle, T., Van Gerven, M.A.J., Kriegeskorte, N., & Kietzmann, T.C. (2023). The neuroconnectionist research programme. Nature Reviews Neuroscience, 24, 431-450.

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

Ali, A., Ahmad, N., Groot, E.D., van Gerven, M.A.J., & Kietzmann, T.C. (2022). Predictive coding is a consequence of energy efficiency in recurrent neural networks. Patterns, 3(12): 100639.

Gert, A.L., Ehinger, B.V., Timm, S., Kietzmann, T.C., & König, P. (2022). WildLab: A naturalistic free viewing experiment reveals previously unknown electroencephalography signatures of face processing. European Journal of Neuroscience, 56(11): 6022–6038.

Mehrer, J., Spoerer, C.J., Jones, E.C., Kriegeskorte, N., & Kietzmann, T.C. (2021). An ecologically motivated image dataset for deep learning yields better models of human vision. Proceedings of the National Academy of Sciences, 118(8), e2011417118.

Storrs, K.R., Kietzmann, T.C., Walther, A., Mehrer, J., & Kriegeskorte, N. (2021). Diverse deep neural networks all predict human IT well, after training and fitting. Journal of Cognitive Neuroscience, 33(10), p. 2044-2064.

Fjell, Anders M., Øystein Sørensen, Inge K. Amlien, David Bartrés-Faz, Andreas M. Brandmaier, Nikolaus Buchmann, Ilja Demuth et al. (2021). Poor Self-Reported sleep is related to regional cortical thinning in aging but not memory Decline. Results From the Lifebrain Consortium. Cerebral Cortex, 31(4), 1953-1969.

Fjell, A., Grydeland, H., Wang, Y., …, & Walhovd, K.B. (2021). The genetic organization of subcortical volumetric change is stable throughout the lifespan. eLife, 2021;10:e66466.

Whittaker, L., Kietzmann, T.C., Kietzmann, J., & Dabirian, A. (2020). All around me are synthetic faces: the Mad World of AI-generated Media. IT Professional, 22, pp. 90-99.

Kietzmann, J., Lee, L.W., McCarthy, I.P., & Kietzmann, T.C. (2020). Deepfakes: Trick or treat? Business Horizons, 63(2), 135-146.

Mehrer, J., Spoerer, C. J., Kriegeskorte, N. & Kietzmann, T. C. (2020). Individual differences among deep neural network models. Nature Communications, 11(1), 5725.

Spoerer, C.J., Kietzmann, T.C., Mehrer, J., Charest, I., & Kriegeskorte, N. (2020). Recurrent neural networks can explain flexible trading of speed and accuracy in biological vision. PLoS Computational Biology, 16(10), e1008215.

Paschen, J., Kietzmann, J., & Kietzmann, T.C. (2019). Artificial intelligence (AI) and its implications for market knowledge in B2B marketing. Journal of Business and Industrial Marketing.

Kietzmann, T.C., McClure, P., & Kriegeskorte, N. (2019). Deep neural networks in computational neuroscience. In Oxford Research Encyclopedia of Neuroscience. Oxford University Press.

Kietzmann, T.C., Spoerer, C.J., Sörensen, L., Cichy, R.M., Hauk, O., & Kriegeskorte, N. (2019). Recurrence is required to capture the representational dynamics of the human visual system. Proceedings of the National Academy of Sciences, 116(43): 21854–21863.

Fjell, A.M., Sørensen, O., Amlien, I.K., Bartrés-Faz, D., Bros, D.M., Demuth, I., Drevon, C.A., Düzel, S., Ebmeier, K.P., Idland, A., Kietzmann, T.C., Kievit, R., Kühn, S., Lindenberger, U., Mowinckel, A.M., Nyberg, L., Price, D., Sexton, C.E., Solé-Padullés, C., Pudas, S., Sederevicius, D., Suri, S., Wagner, G., Watne, L.O., Westerhausen, R., Zsoldos, E., Walhovd, K.B. (2019). Self-reported sleep relates to hippocampal atrophy across the adult lifespan. Results from the Lifebrain consortium. Sleep, 43(5), zsz280.

Wilming, N., Onat, S., Ossandón, J.P., Açık, A., Kietzmann, T.C., Kaspar, K., Gameiro, R.R., Vormberg, A., König, P. (2017). An extensive dataset of eye movements during viewing of complex images. Nature Scientific Data, 4, p. 1-11.

Wilming, N., Kietzmann, T.C., Jutras, M., Xue, C., Treue, S., Buffalo, E., & König, P. (2017). Differential contribution of low and high-level image content to eye movements in monkeys and humans. Cerebral Cortex, 27(1), p. 279-293.

Kietzmann, T.C., Gert, A.L., & König, P. (2017). Representational dynamics of facial viewpoint encoding. Journal of Cognitive Neuroscience, 4, p. 637-651.

Kietzmann, T.C., Ehinger, B.V., Porada, D., Engel, A., & König, P. (2016). Extensive Training Leads to Temporal and Spatial Shifts of Cortical Activity Underlying Visual Category Selectivity. NeuroImage, 134, p. 22-34.

König, P., Wilming, N., Kietzmann, T.C., Ossandón, J.P., Onat, S., Ehinger, V.E., Gameiro, R.R., & Kaspar, K. (2016). Eye movements as a window to cognitive processes. Journal of Eye Movement Research, 9(5), 3, p. 1-16.

Kietzmann, T.C., & König, P. (2015). Effects of Contextual Information and Stimulus Ambiguity on Overt Visual Sampling Behavior. Vision Research, 110, p.76-86.

Kietzmann, T.C., Poltoratski, S., König, P., Blake, R., Tong, F., & Ling, S. (2015). The Occipital Face Area Is Causally Involved in Facial Viewpoint Perception. Journal of Neuroscience, 35(50), p. 16398-16403.

Kietzmann, T.C., Swisher, J., König, P., & Tong, F. (2012). Prevalence of Selectivity for Mirror-Symmetric Views of Faces in the Ventral and Dorsal Visual Pathways. Journal of Neuroscience, 32(34), p. 11763-11772.

Wilming, N., Betz, T., Kietzmann, T.C., & König, P. (2011). Measures and limits of models of fixation selection. PLoS One 6(9), p. 1-19.

Kietzmann, T.C., Geuter, S., & König, P. (2011). Overt Visual Attention as a Causal Factor of Perceptual Awareness. PLoS One, 6(7), p. 1-9.

Betz, T., Kietzmann, T.C., Wilming, N., & König, P. (2010). Investigating Task-Dependent Top-Down Effects on Overt Visual Attention. Journal of Vision, 10(3), p. 1-14.

Kietzmann, T.C., & König, P. (2010). Perceptual learning of parametric face categories leads to the integration of high-level class-based information but not to high-level pop-out. Journal of Vision, 10(13), p.1-14.

Kietzmann, T.C., Lange, S. & Riedmiller, M. (2009). Computational Object Recognition: A Biologically Motivated Approach. Biological Cybernetics, 100, p. 59-79.

Kietzmann, T.C., Lange, S. & Riedmiller, M. (2008). Incremental GRLVQ: The Case of Object Recognition. Neurocomputing, 71, p. 2868-2879.

Bosch V., Anthes, D., Doerig, A., Gütlin, D., Thorat, S., König P., & Kietzmann, T.C. (2025). CorText-AMA: brain-language fusion as a new tool for probing visually evoked brain responses. CCN 2025, Amsterdam, Netherlands.

Amme, C., Sulewski, P., Braatz, M., König, P., & Kietzmann, T.C. (2025). Encoding of Fixation-Specific Visual Information: No Evidence of Information Carry-Over between Fixations. CCN 2025, Amsterdam, Netherlands.

Nolte, D., Grasso-Cladera, A., Zaidan, A., Dubrall, A., Akkaya, A., Kietzmann, T.C., & König, P. (2025). Exploring Saccade-Onset Event-Related Potentials for Face Perception in the Real World. ECVP 2025, Mainz, Germany.

Lu, Z., Thorat, S., Cichy, R.M., Kietzmann, T.C. (2025). Getting into Shape: The Impact of Early Visual Development on Object Recognition. CCN 2025, Amsterdam, Netherlands.

Thorat, S., Doerig, A., Kroner, A., Amme, C., & Kietzmann, T.C. (2025). Glimpse prediction fosters graph-oriented scene representations aligned with the ventral visual cortex. CCN 2025, Amsterdam, Netherlands and ECVP 2025, Mainz, Germany.

Furtak, M., Pätzold, F., Kietzmann, T.C., Kärcher, S., & König, P. (2025). Helping Blind People Grasp: Enhancing a Tactile Bracelet with an Automated Hand Navigation System. ECVP 2025, Mainz, Germany.

Bai, S., Sulewski, P., Amme, C., König, P., Kietzmann, T.C., Peelen, M. V., & Spaak, E. (2025). Neural oscillations encode context-based informativeness during naturalistic free viewing. CCN 2025, Amsterdam, Netherlands and NVP 2025.

Nortmann, T., Sulewski, P., & Kietzmann, T.C. (2025). Predictive remapping and allocentric coding as consequences of energy efficiency in recurrent neural network models of active vision. CCN 2025, Amsterdam, Netherlands.

Piefke, L., Doerig, A., Kietzmann, T.C. & Thorat, S. (2024). Computational characterization of the role of an attention schema in controlling visuospatial attention. CogSci 2024, Rotterdam.

Anthes D, Thorat S, König P, Kietzmann T.C. (2024). Continual learning in artificial neural networks as a computational framework for understanding representational drift in biological systems. CCN 2024, Cambridge, USA.

Bosch V., Gütlin, D., Doerig, A., Anthes, D., Thorat, S., König P., Kietzmann, T.C. (2024). CorText: large language models for cross-modal transformations from visually evoked brain responses to text captions. CCN 2024.

Sulewski, P., Amme, C., Hebart, M., König, P., & Kietzmann, T.C. (2024). Gazing into memory: Active vision is timed to stabilise cortical representations for fixation-based memory encoding. ECVP 2024, Aberdeen, Scotland.

Anthes D*, Thorat S*, König P, Kietzmann T.C. (2024). Keep moving: identifying task-relevant subspaces to maximise plasticity for newly learned tasks. CoLLAs 2024.

Amme, C., Sulewski, P., Spaak, E., Hebart, M. N., König, P., & Kietzmann, T.C. (2024). Saccade onset, not fixation onset, best explains early responses across the human visual cortex during naturalistic vision. ECVP 2025, Mainz, Germany.

Sulewski, P., König, P., Kriegeskorte, N., & Kietzmann, T.C. (2023). Analyses of the neural population dynamics during human object vision reveal two types of representational echoes that reverberate across the visual system. CuttingGardens (CuttingMEEG) 2023, Frankfurt, DE.

Thorat S, Doerig A, Kietzmann TC (2023). Characterising representation dynamics in recurrent neural networks for object recognition. CCN 2023, Oxford, GB.

Anthes D, Thorat S, König P, Kietzmann TC (2023). Diagnosing Catastrophe: Large Parts of Accuracy Loss in Continual Learning Can Be Accounted for by Readout Misalignment. CCN 2023, Oxford, GB.

Lu, Z., Doerig, A., Bosch, V., Krahmer, B., Kaiser, D., Cichy, R.M., Kietzmann, T.C. (2023). End-to-end topographic networks as models of cortical map formation and human visual behaviour: moving beyond convolutions. Analytical Connectionism Summer School 2023, Gatsby Unit UCL.

Doerig, A., Kirubeswaran, O.R., Kietzmann, T.C. (2023). Keep moving: sensorimotor integration of fixational eye-movements yields human-like superresolution in recurrent neural networks. CCN 2023, Oxford, GB.

Doerig, A., Lindh, D., Lebeau, E., Kietzmann, T.C., Sligte, I.G., Shapiro, K.L., Ian Charest, I. (2023). Representational similarity across visual cortex explains the attentional blink. ASSC 2023, New York, USA.

Karapetian A., Boyanova, A., Pandaram, M., Obermayer, K., Kietzmann, T.C., & Cichy, R.M. (2023). Scene representations underlying categorization behaviour emerge 100 to 200 ms after stimulus onset. Vision Science Society Meeting 2023, St. Pete Beach, Florida, USA.

Lu, Z., Doerig, A., Bosch, V., Krahmer, B., Kaiser, D., Cichy, R., Kietzmann, T.C. (2023). The brain can’t copy-paste: End-to-end topographic neural networks as a way forward for modelling cortical map formation and behaviour. CCN 2023, Oxford, GB.

Thorat, S., Aldegheri, G. , Kietzmann, T.C. (2022). Category-orthogonal object features guide information processing in recurrent neural networks trained for object categorization. ECVP 2022, Nijmegen, NL.

Jozwik, K. M., Kietzmann, T. C., Cichy R. M., Kriegeskorte, N. & Mur, M. (2022). Deep neural networks and visuo-semantic models explain complementary components of human ventral-stream representational dynamics. SFN 2022, San Diego, USA.

Doerig, A., Krahmer, B., Bosch, V. & Kietzmann, T.C. (2022). Emergence of topographic organization in a non-convolutional deep neural network. ECVP 2022, Nijmegen, NL.

Doerig, A., Krahmer, B., Bosch, V., & Kietzmann, T.C. (2022). Emergence of topography in a non-convolutional deep neural network. ECVP 2022, Nijmegen, NL.

Smith, F. W., Mansfield, C., Kietzmann, T.C., Van den Bosch, J., Charest, I., Mur, M. & Kriegeskorte, N. (2022). Neural representation of occluded objects in visual cortex. 22nd Annual Meeting of the Organization for Human Brain Mapping, Glasgow, Scotland.

Mansfield, C., Kietzmann, T.C., Van den Bosch, J., Charest I., Mur M., Kriegeskorte N. & Smith F. W. (2022). Neural representation of occluded objects in visual cortex. Annual Meeting of the British Association for Cognitive Neuroscience, University of Birmingham, UK. Winner Best Poster Prize.

Doerig, A., Kietzmann, T.C. (2022). Neuroconnectionism as a progressive research program for neuroscience: a case study of cortical map formation. ECVP 2022, Nijmegen, NL.

Doerig, A., & Kietzmann, T.C. (2022). The neuroconnectionism research programme. ECVP 2022, Nijmegen, NL.

Ólafsdóttir, I. M., Albertsdóttir, S. L., Ásgeirsdóttir, U. A., Kietzmann, T. C., & Sigurdardottir, H. M. (2022). Visual and semantic factors in object recognition. Journal of Vision, 22(14), 3928-3928.

Kietzmann, T.C. (2022). Visual Neuroscience Meets Machine Learning. ECVP 2022, Nijmegen, NL.

Kietzmann, T.C. (2021). An ecologically motivated image dataset for deep learning yields better models of human vision. NeurIPS workshop (invited contribution): ImageNet: past, present, and future, online format.

Sulewski, P., König, P., Kriegeskorte, N., & Kietzmann, T.C. (2021). Analyses of the neural population dynamics during human object vision reveal two types of representational echoes that reverberate across the visual system. ECVP 2022, Nijmegen, NL.

Sulewski, P., König, P., Kriegeskorte, N., & Kietzmann, T.C. (2021). Analyses of the neural population dynamics during human object vision reveal two types of representational echoes that reverberate across the visual system. Neuromatch 4.0, online format.

Thorat, S., Aldegheri, G., & Kietzmann, T.C. (2021). Category-orthogonal object features guide information processing in recurrent neural networks trained for object categorization. Montreal AI and Neuroscience conference MAIN, online format [best student graphical abstract award].

Thorat, S., Aldegheri, G., & Kietzmann, T.C. (2021). Category-orthogonal object features guide information processing in recurrent neural networks trained for object categorization. NeurIPS SVRHM workshop, online format.

Jozwik, K.M., Kietzmann, T.C., Kriegeskorte, N., & Mur, M. (2021). Deep neural networks and visuo-semantic models explain complementary components of human ventral-stream representational dynamics. Mathematics Of Neuro-Science, Technology and Engineering, Rhodes, Greece.

Doerig, A., Krahmer, B. & Kietzmann, T.C. (2021). Emergence of topographic organization in a non-convolutional deep neural network. Montreal AI and Neuroscience conference MAIN, online format [best postdoctoral abstract award].

Doerig, A., Krahmer, B. & Kietzmann, T.C. (2021). Emergence of topographic organization in a non-convolutional deep neural network. Neuromatch 4.0, online format.

Ólafsdóttir, I.M., Albertsdóttir, S.L., Ásgeirsdóttir1, U.A., Kietzmann, T.C., & Sigurdardottir, H.M. (2021). Mapping the dimensions of object perception. OPAM conference on Object Perception, visual Attention, and visual Memory, online format.

Ali, A., Ahmad, N., de Groot, E., van Gerven, M., & Kietzmann, T.C. (2021). Predictive coding is a consequence of energy efficiency in recurrent neural networks. Champalimaud Research Symposium, Lisbon, Portugal.

Ali, A., Ahmad, N., de Groot, E., van Gerven, M., & Kietzmann, T.C. (2021). Predictive coding is a consequence of energy efficiency in recurrent neural networks. ECVP 2022, Nijmegen, NL.

Ali, A., Ahmad, N., de Groot, E., van Gerven, M., & Kietzmann, T.C. (2021). Predictive coding is a consequence of energy efficiency in recurrent neural networks. Neuromatch 4.0, online format.

Hernandez-Garcia, A., König, P., & Kietzmann, T.C. (2020). Learning robust visual representations using data augmentation invariance. ICLR workshop: Bridging AI and Cognitive Science, Addis Ababa, Ethiopia.

Gert, A.L., Ehinger, B.V., Kietzmann, T.C., & König, P. (2020). The face-attraction bias in free viewing. Eye Tracking Research and Applications.

Borgeest, S., Kietzmann, T.C., Fuhrmann, D., Henson, R., & Kievit, R. (2019). Detailed shape measures capture age-related neural differences better than volumetric approaches. The Organization for Human Brain Mapping (OHBM) Annual Meeting, Rome, Italy.

Hernandez-Garcia, A., König, P., & Kietzmann, T.C. (2019). Learning robust visual representations using data augmentation invariance. Cognitive Computational Neuroscience Meeting, Berlin, Germany.

Hernandez-Garcia, A., König, P., & Kietzmann, T.C. (2019). Learning robust visual representations using data augmentation invariance. Computational Cognition, Osnabrück, Germany.

Spoerer, C.J., Kietzmann, T.C., & Kriegeskorte, N. (2019). Recurrent networks can recycle neural resources to flexibly trade speed for accuracy in visual recognition. Cognitive Computational Neuroscience Meeting, Berlin, Germany.

Lin, B., Kriegeskorte, N., Mur, M., & Kietzmann, T.C. (2019). Visualizing Representational Dynamics with Multidimensional Scaling Alignment. Cognitive Computational Neuroscience Meeting, Berlin, Germany.

Gert, A.L., Ehinger, B.V., Timm, S, König, P, & Kietzmann, T.C. (2019). Wild lab: Characterizing face-selective ERPs under more natural conditions. European Conference on Eye Movements (ECEM), Alicante, Spain.

Gert, A.L., Ehinger, B.V., Timm, S, König, P, & Kietzmann, T.C. (2019). Wild lab: Characterizing face-selective ERPs under more natural conditions. European Conference on Visual Perception (ECVP), Leuven, Belgium.

Mehrer, J., Kriegeskorte, N., & Kietzmann, T.C. (2018). Beware the beginnings: intermediate and higher-level representations in deep neural networks are strongly affected by weight initialisation. Cognitive Computational Neuroscience Meeting, Philadelphia, USA.

Mehrer, J., Kriegeskorte, N., & Kietzmann, T.C. (2018). Deep Neural Networks Trained on Ecologically Relevant Categories Better explain human IT. European Conference on Visual Perception (ECVP), Trieste, Italy.

Hernandez, A., Mehrer, J., Kriegeskorte, N., König, P.*, & Kietzmann, T.C.* (2018). Deep neural networks trained with heavier data augmentation learn features closer to representations in hIT. Cognitive Computational Neuroscience Meeting, Philadelphia, USA.

McClure, P., Kietzmann, T.C., Mehrer, J., & Kriegeskorte, N. (2018). Modelling Human Visual Uncertainty using Bayesian Deep Neural Networks. Cognitive Computational Neuroscience Meeting, Philadelphia, USA.

Kietzmann, T.C., Spoerer, C.J., Sörensen, L.K.A., Cichy, R.M., Hauk, O., & Kriegeskorte, N. (2018). Representational dynamics in the human ventral stream captured in deep recurrent neural nets. Cognitive Computational Neuroscience Meeting, Philadelphia, USA.

Kietzmann, T.C., Hauk, O., & Kriegeskorte, N. (2017). A cross-validation approach to estimate the relative signal- and noise-content of ICA components. MEG UK 2017, Oxford, UK.

Mehrer, J., Kietzmann, T.C., & Kriegeskorte, N. (2017). Deep Neural Networks Trained on Ecologically Relevant Categories Better explain human IT. Cognitive Computational Neuroscience Meeting, New York, USA.

Gert, A.L., Kietzmann, T.C., Noll, P., & König, P. (2017). Development of a step sensor interface for mobile EEG experiments. International Conference of Cognitive Neuroscience, Amsterdam, Netherlands.

Sörensen, L.K.A., Kietzmann, T.C., Cichy R.M., Hauk, O., & Kriegeskorte, N. (2017). Representational dynamics of object processing in source-reconstructed MEG data. International Conference of Cognitive Neuroscience, Amsterdam, Netherlands.

Sörensen, L.K.A., Kietzmann, T.C., Cichy R.M., Hauk, O., & Kriegeskorte, N. (2017). What can source-reconstructed MEG data tell us about representational dynamics during object perception? cuttingEEG 2017, Glasgow, UK.

Ehinger, B.V., Kietzmann, T.C., Porada, D., Engel, A.K., & König, P. (2016). A spatiotemporal analysis of MEG Adaptation Paradigms applied to extensive Visual Category Learning. Organization for Human Brain Mapping Meeting, Geneva, Switzerland.

Schöning, J., Gert, A.L., Acik, A., Kietzmann, T.C., Heidemann, G., & König, P. (2016). Exploratory Multimodal Data Analysis with Standard Multimedia Player. International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, Rome, Italy.

Gert, A.L., Kietzmann, T.C., & König, P. (2016). Face-responsive ERP components show time-varying viewing angle preferences. European Conference on Visual Perception, Barcelona, Spain.

Ossandón, J.P., Kietzmann, T.C., Timm, S., König, P. (2015). A direct electrophysiological demonstration of object based sensory processing. European Conference on Visual Perception, Liverpool, UK.

Kietzmann, T.C., Gert, A.L., & König, P. (2015). Representational dynamics of facial viewpoint encoding. Vision Science Society Meeting 2015, St. Pete Beach, USA.

Kietzmann, T.C., Ling, S., Poltoratski, S., König, P., Blake, R., & Tong, F. (2014). The Occipital Face Area is Causally Involved in Viewpoint Symmetry Judgments of Faces. Vision Science Society Meeting 2014, St. Pete Beach, USA.

Kietzmann, T.C., Wahn, B., König, P., & Tong, F. (2013). Face selective areas in the human ventral stream exhibit a preference for 3/4 views in the fovea and periphery. European Conference on Visual Perception 2013, Bremen, Germany.

Kietzmann, T.C., Ehinger, B., Porada, D., Engel, A., & König, P. (2013). From stimulus onset to category selectivity in 100ms: category-selective visually evoked responses as a result of extensive category learning. Vision Science Society Meeting 2013, Naples, USA.

Kietzmann, T.C., Ehinger, B., Porada, D., Engel, A., & König, P. (2013). Perceptual Learning Leads to Category Selectivity 100ms after Stimulus Onset. European Conference on Visual Perception 2013, Bremen, Germany.

Kietzmann, T.C., Swisher, J., König, P., & Tong, F. (2012). Selectivity for Mirror-Symmetric Views of Faces in the Ventral and Dorsal Streams of the Human Visual System. Vision Science Society Meeting 2012, Naples, USA.

Kietzmann, T.C., & König, P. (2010). Parametric Faces in Pop-Out Paradigm: When Class Information Becomes a Feature. KogWis 2010, Potsdam, Germany.

Kietzmann, T.C. (2009). Philosophical Accounts of Causal Explanation and the Scientific Practice of Psychophysics. EPSA Philosophy of Science: Amsterdam 2009, Chapter 11, p.1-11, Amsterdam, Netherlands.

Geuter S., Kietzmann, T.C., & König, P. (2009). Pupil Dilation at the Time of Perceptual Events and Decision-Making. Rovereto Attention Workshop 2009, Rovereto, Italy.

Schreiber, C., Betz, T., Wilming, N., Kietzmann, T.C., & König, P. (2009). Task-effects on Viewing Behavior Examined in School Children. 8th Göttingen Meeting of the German Neuroscience Society 2009, Göttingen, Germany.

Kietzmann, T.C., & Riedmiller, M. (2009). The Neuro Slot Car Racer: Reinforcement Learning in a Real World Setting. International Conference on Machine Learning and Applications 2009, Miami Beach, USA.

Kietzmann, T.C., Geuter, S., & König, P. (2009). The Role of Overt Visual Attention in the Process of Perceptual Formation. Rovereto Attention Workshop 2009, Rovereto, Italy.

König, P., Kühnberger, K.U., & Kietzmann, T.C. (2014). A unifying approach to high- and low-level cognition. In Models, Simulations, and the Reduction of Complexity. (pp. 117-141). De Gruyter.

Kietzmann, T.C. (upcoming). Large language models as a key technology for analysing and reading brains. Keynote at MEi:CogSci, University of Vienna, Vienna, Austria.

Kietzmann, T.C. (2026). NeuroAI: biological intelligence meets artificial intelligence. Law and Technology Colloquium, Osnabrück University, Osnabrück, Germany.

Kietzmann, T.C. (2026). NeuroAI: the synergy between machine learning and neuroscience. UCL NeuroAI Talk Series, London, UK.

Kietzmann, T.C. (2026). NeuroKI: Grundlagenforschung zwischen den Welten. Deutsche Bundesbank, Frankfurt, Germany.

Kietzmann, T.C. (2026). The intertwined quest for understanding biological intelligence and creating artificial intelligence. AIM for Brain Keynote, Münster University, Münster, Germany.

Bosch, V., Anthes, D., Doerig, A., Thorat, S., König, P., Kietzmann, T.C. (2025). Brain-language fusion enables interactive neural readout and in-silico experimentation. Prof. Naselaris’ lab, University of Minnesota; Prof. Kriegeskorte’s lab, Columbia University, New York; Workshop for Automated Scientific Discovery of Mind and Brain, Princeton University.

Kietzmann, T.C. (2025). Do embodied agents learn to see the world differently? IKW Lightning Talks, Osnabrück University, Osnabrück, Germany.

Kietzmann, T.C. (2025). NeuroAI. IKW Lightning Talks, Osnabrück University, Osnabrück, Germany.

Kietzmann, T.C. (2025). NeuroAI: the synergy between machine learning and neuroscience. Universiteit van Amsterdam (UvA), Amsterdam, Netherlands.

Kietzmann, T.C. (2025). NeuroKI: Grundlagenforschung zwischen den Welten. VDI Zukunftsgespräch, Berlin, Germany.

Kietzmann, T.C. (2025). The brain can’t copy-paste. End-to-end topographic networks as models of map formation and human visual behaviour. CCN 2025: Modeling the Physical Brain, Amsterdam, Netherlands.

Kietzmann, T.C. (2025). Where next? [after a decade of ANN models of primate vision]. Tübingen Systems Neuroscience Symposium, Tübingen, Germany.

Kietzmann, T.C. (2024). Die Anatomie künstlicher Intelligenz. Industrie und Handelskammer, Osnabrück, Germany.

Kietzmann, T.C. (2024). Emerging features: computational insights from normative models of primate vision. CIFAR Learning in Machines & Brains program meeting, Zürich, Switzerland.

Kietzmann, T.C. (2024). Large Language Models for modelling human vision. SFB retreat Keynote, Rauischholzhausen Castle, Germany.

Kietzmann, T.C. (2024). Large Language Models offer a rich representational format for understanding the transformation of visual information in the human brain. Science of Intelligence Excellence Cluster, Berlin, Germany.

Kietzmann, T.C. (2024). Modelling vision in the face of large language models. ECVP Keynote, Aberdeen, Scotland.

Kietzmann, T.C. (2024). NeuroAI as a Language for Expressing Computational Hypotheses about Brain Function. UCSB, Santa Barbara, California, USA.

Kietzmann, T.C. (2023). Are categories the right path towards understanding primate vision? IKW Lightning Talks, Osnabrück, Germany.

Thelen, T. & Kietzmann, T.C. (2023). chatGPT verstehen: Hintergründe, Chancen und Perspektiven (auch) für die Bildung. Keynote at GMA Jahrestagung, Osnabrück, Germany.

Kietzmann, T.C. (2023). chatGPT verstehen. Hintergründe, Chancen, Perspektiven. Sievers World, Osnabrück, Germany.

Kietzmann, T.C. (2023). Next steps in modelling human vision: topographies and semantics. Netherlands Institute for Neuroscience, Amsterdam, Netherlands.

Kietzmann, T.C. (2023). Reports from our neuroconnectionist frontier: topographies and semantics. Cognitive Computational Neuroscience Keynote, Oxford, UK.

Kietzmann, T.C. (2023). The anatomy of AI. Youth Empowering Labs, Osnabrück, Germany.

Kietzmann, T.C. (2023). The neuroconnectionist research programme. Lebenswissenschaftliches Kolleg der Studienstiftung des deutschen Volkes, online event.

Kietzmann, T.C. (2023). The neuroconnectionist research programme. Using DNNs to study Visual Cognition, University of Amsterdam (UVA), Amsterdam, NL.

Kietzmann, T.C. (2022). Catching brains with deep nets. Deep learning as a framework for understanding human vision. Campus-Institut Data Science (CIDAS), University of Göttingen, Göttingen, Germany.

Kietzmann, T.C. (2022). Deep recurrent neural networks as a modelling framework for understanding human vision. Mellichamp Initiative in Mind & Machine Intelligence Summit, UC Santa Barbara, USA.

Kietzmann, T.C. (2022). Deep recurrent neural networks as a modelling framework for understanding the dynamic computations of human vision. Osnabrück Deep Learning Lecture, Osnabrück, Germany.

Kietzmann, T.C. (2022). Deep recurrent neural networks as a modelling framework for understanding the dynamic computations of human vision. University of York seminar, York, UK.

Kietzmann, T.C. (2022). Neuroconnectionism as a framework for understanding neural information processing. International Interdisciplinary Computational Cognitive Science Summer School, Tübingen, Germany.

Kietzmann, T.C. (2022). Recurrence as a key ingredient for understanding robust human object recognition. CVPR NeuroVision Workshop, Seattle, USA.

Kietzmann, T.C. (2022). The neuroconnectionist research programme. GeSiMEx Symposium “Computational Mechanisms in Brains and Machines: Simplicity & Generalizability”, Berlin, Germany.

Kietzmann, T.C. (2021). Deep recurrent neural networks as a modelling framework for understanding the dynamic computations of human vision. Neural Information Processing Colloquium, Tübingen, Germany.

Kietzmann, T.C. (2021). Recurrence as a key architectural component for modelling the dynamics of human object recognition. Telluride Neuromorphic Cognition Engineering Workshop, online, worldwide.

Kietzmann, T.C. (2020). Deep neural networks as a framework for understanding the dynamic computations of the human visual system. Mind and Machine Seminar, Bristol University, Bristol, UK.

Kietzmann, T.C. (2020). Deep neural networks as a framework for understanding the dynamic computations of the human visual system. Neurospin Conference, École des Neurosciences Paris, Paris, France.

Kietzmann, T.C. (2020). Deep neural networks as a framework for understanding the dynamic computations of the human visual system. Oxford Autumn School in Neuroscience, Oxford, UK.

Kietzmann, T.C. (2020). Deep neural networks as a model of visual inference in the brain. Göttingen Neuroscience Seminar, Göttingen University, Göttingen, Germany.

Kietzmann, T.C. (2020). It’s about time. Modelling human visual inference with deep recurrent neural networks. NeurIPS SVRHM Workshop, NeurIPS Conference.

Kietzmann, T.C. (2020). Neuro-connectionism: how neuro-inspired machine learning leads to insights into human vision. GeSiMEx Workshop, University Magdeburg, Witten/Herdecke, Germany.

Kietzmann, T.C. (2020). Recurrent connectivity: a key towards understanding and mirroring robust human object recognition. Vanderbilt Cognitive Neuroscience Seminar Series, Vanderbilt University, Nashville, TN, USA.

Kietzmann, T.C. (2019). Deep (recurrent) neural networks for understanding the dynamic computations of the human visual system. Bernstein Conference 2019, Deep Learning in Computational Neuroscience, Berlin, Germany.

Kietzmann, T.C. (2019). Deep learning as a novel framework for understanding the dynamic computations of the human visual system. Imperial College London, London, UK.

Kietzmann, T.C. (2019). Deep neural networks as a framework for understanding the dynamic computations of the human visual system. IAS Seminar, Jülich Supercomputing Centre, Jülich, Germany.

Kietzmann, T.C. (2019). From pixels to semantics: machine learning as a key to understanding the computations of the human visual system. Data Analytics and Computational Modelling, Goethe University, Frankfurt, Germany.

Kietzmann, T.C. (2019). From pixels to semantics: using deep learning to generate insight into neural computations. Deep Learning Autumn School, University of Amsterdam, Amsterdam, Netherlands.

Kietzmann, T.C. (2019). Inter-individual differences among deep neural network models. Symposium: The organisational principles of the visual ventral stream, University of Cambridge, Cambridge, UK.

Kietzmann, T.C. (2019). Understanding vision at the interface of computational neuroscience and artificial intelligence. Computational Cognition Workshop, Osnabrück University, Osnabrück, Germany.

Kietzmann, T.C. (2019). Understanding vision at the interface of computational neuroscience and artificial intelligence. Keynote at BMVA technical meeting: Visual Image Interpretation in Humans and Machines: Machines that see like us?, London, UK.

Kietzmann, T.C. (2019). Understanding vision at the interface of computational neuroscience and machine learning. Birmingham University, Birmingham, UK.

Kietzmann, T.C. (2018). Computational Neuroscience 2.0: How Deep Learning Will Shape How We Understand Computations in the Brain. Matrix Institute for Applied Data Science, University of Victoria, Victoria, Canada.

Kietzmann, T.C. (2018). Deep learning in cognitive computational neuroscience: a gentle introduction. University of Cambridge, Cambridge, UK.

Kietzmann, T.C. (2018). From pixels to semantics: machine learning as a key to understanding the dynamic computations along the human ventral stream. Chaucer Club, University of Cambridge, Cambridge, UK.

Kietzmann, T.C. (2018). Improving DNNs as models of the human ventral stream: a better visual diet and recurrent computations. ECVP 2018 Deep Learning Symposium, Trieste, Italy.

Kietzmann, T.C. (2018). Investigating time-varying representational trajectories using MEG representational dynamics analysis. 2nd Cambridge Representational Similarity Analysis and Advanced Computational Methods Workshop (RSA2018), Cambridge, UK.

Kietzmann, T.C. (2018). Machine learning as a key to understanding the dynamic computations along the human ventral stream. SISSA International School for Advanced Studies, Trieste, Italy.

Kietzmann, T.C. (2018). Recurrence required to capture the dynamic computations of the human ventral visual stream. National Institute of Mental Health, NIH, Bethesda, USA.

Kietzmann, T.C. (2018). Representational dynamics in the human ventral stream captured in recurrent DNNs. Bernstein Conference 2018, Representational Dynamics Workshop, Berlin, Germany.

Kietzmann, T.C. (2017). Estimating the relative signal- and noise-content of ICA components, Institute of Cognitive Science, Osnabrück University, Osnabrück, Germany.

Kietzmann, T.C. (2017). Machine Learning and Representational Similarity Analysis in Cognitive Neuroscience: 2 day workshop. Karolinska Institute, Stockholm, Sweden.

Kietzmann, T.C. (2016). Dynamic RSA. MRC Cognition and Brain Sciences Unit Methods Day, University of Cambridge, Cambridge, UK.

Kietzmann, T.C. (2016). Viewpoint Invariance in the Brain. Center for Cognitive Neuroscience Berlin Seminar Series, FU Berlin, Berlin, Germany.

Kietzmann, T.C. (2016). Visual Invariance in the Brain. SPECS Synthetic, Perceptive, Emotive and Cognitive Systems group, Universitat Pompeu Fabra, Barcelona, Spain.

Kietzmann, T.C. (2011). The When and the Where of Perceptual Categorization. Category Laboratory, Vanderbilt University, Nashville, USA.

Kietzmann, T.C. (2010). Hierarchical Models of Object Recognition. Vanderbilt Vision Research Center, Vanderbilt University, Nashville, USA.

Kietzmann, T.C. (2009). From Biological Findings to Computational Object Recognition Systems. Models For Invariant Object Recognition and Categorization Symposium. Bochum International Graduate School of Neuroscience, Bochum, Germany.

Lee, L.W., Kietzmann, J., & Kietzmann, T.C. (2020). Deepfakes: five ways in which they are brilliant business opportunities. The Conversation, UK.

Kietzmann, T.C. (2019-today). Member of Skype a Scientist.

Kietzmann, T.C. (2019). What can A.I. and neuroscience learn from each other? Science Night 2019, Cambridge, UK.

Kietzmann, T.C. (2018). Life Story. #scientistandparent eLife series, eLife.

Kietzmann, T.C. (2017). 7 Questions about academic publishing. Brain and Cognitive Sciences Journal, Amsterdam, NL.

Kietzmann, T.C. (2015). Newspaper article: “Osnabrücker Forscher lüftet Geheimnisse des Gehirns” (NOZ, German only).