Preprints
Piefke, L., Doerig, A., Kietzmann, T.C., Thorat, S. (2024). Computational characterization of the role of an attention schema in controlling visuospatial attention. arXiv [Preprint]
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. arXiv preprint arXiv:2308.09431. [Preprint]
Doerig, A., Kietzmann, T.C., Allen, E., Wu, Y., Naselaris, T., Kay, K., & Charest, I. (2022). Semantic scene descriptions as an objective of human vision. arxiv; doi: https://doi.org/10.48550/arXiv.2209.11737 [Preprint]
Fjell, A., Sorensen, O., Amlien, I. K., Bartres-Faz, D., Brandmaier, A., Macia, D., … & Ebmeier, K. (2020). Self-reported sleep problems are related to cortical thinning in aging but not memory decline and amyloid-β accumulation-results from the Lifebrain consortium. bioRxiv. [Preprint]
Journal Publications
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 JN-RM-1424-22; doi: https://doi.org/10.1523/JNEUROSCI.1424-22.2022 [Article]
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 100639; doi: https://doi.org/10.1016/j.patter.2022.100639 [Article][Data]
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, 1– 17, https://doi.org/10.1111/ejn.15824, [Article]
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), doi:https://doi.org/10.1167/jov.22.2.4, [Article]
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 [Article]
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; DOI: 10.1073/pnas.2011417118 [Article][Dataset]
Fjell, Anders M., Øystein Sørensen, Inge K. Amlien, David Bartrés-Faz, Andreas M. Brandmaier, Nikolaus Buchmann, Ilja Demuth et al. “Poor Self-Reported sleep is related to regional cortical thinning in aging but not memory Decline—Results From the Lifebrain Consortium.” Cerebral Cortex 31, no. 4 (2021): 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; DOI: 10.7554/eLife.66466 [Article]
Mehrer, J., Spoerer, C. J., Kriegeskorte, N. & Kietzmann, T. C. (2020). Individual differences among deep neural network models. Nature Communications, 11(1), 5725. https://doi.org/10.1038/s41467-020-19632-w [Article]
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. https://doi.org/10.1371/journal.pcbi.1008215 [Article]
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. [Article][File]
Kietzmann, J., Lee, L.W., McCarthy, I.P., & Kietzmann, T.C. (2020). Deepfakes: Trick or treat? Business Horizons, 63(2), 135-146. [Article]
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. [Preprint][Article]
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, p. 1-10 [Article]
Kietzmann, T.C., McClure, P., & Kriegeskorte, N. (2019). Deep neural networks in computational neuroscience. In Oxford Research Encyclopedia of Neuroscience. Oxford University Press. [Article]
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., Gert, A.L., & König, P. (2017). Representational dynamics of facial viewpoint encoding. Journal of Cognitive Neuroscience, 4, p. 637-651 [Article] [PDF]
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 [Article]
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 [Article] [PDF]
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 [Article]
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 [Article]
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 [Article] [PDF]
Kietzmann, T.C., & König, P. (2015). Effects of Contextual Information and Stimulus Ambiguity on Overt Visual Sampling Behavior, Vision Research, 110, p.76-86 [Article]
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 [Article] [PDF]
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 [Article] [PDF]
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 [Article] [PDF]
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 [Article]
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 [Article]
Kietzmann, T.C., Lange, S. & Riedmiller, M. (2009). Computational Object Recognition: A Biologically Motivated Approach, Biological Cybernetics, 100, p. 59-79 [Article]
Kietzmann, T.C., Lange, S. & Riedmiller, M. (2008). Incremental GRLVQ: The Case of Object Recognition, Neurocomputing, 71, p. 2868-2879 [Article]
Conference Contributions
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. CuttingGardens (CuttingMEEG) 2023, Frankfurt, DE
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
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
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, Doerig A, Kietzmann TC (2023) Characterising representation dynamics in recurrent neural networks for object recognition. 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
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
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., 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., Krahmer, B., Bosch, V., & Kietzmann, T.C. (2022). Emergence of topography in a non-convolutional deep neural network. ECVP 2022, Nijmegen, NL
Doerig, A., & Kietzmann, T.C. (2022). The neuroconnectionism research programme. ECVP 2022, Nijmegen, NL
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
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
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
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
Kietzmann, T.C. (2022). Visual Neuroscience Meets Machine Learning. ECVP 2022, Nijmegen, NL
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.
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
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
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]
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 [Article]
Doerig, A., Krahmer, B. & Kietzmann, T.C. (2021). Emergence of topographic organization in a non-convolutional deep neural network, 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 [Article] [best student graphical abstract award]
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
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
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
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
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
Hernandez-Garcia, A., König, P., & Kietzmann, T.C. (2019). Learning robust visual representations using data augmentation invariance. Computational Cognition, Osnabrück, Germany
Hernandez-Garcia, A., König, P., & Kietzmann, T.C. (2019). Learning robust visual representations using data augmentation invariance. 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
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
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
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
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
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
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
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
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
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
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
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). What can source-reconstructed MEG data tell us about representational dynamics during object perception? cuttingEEG 2017, Glasgow, UK
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
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
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
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., 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., 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., 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
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
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., Geuter, S., & König, P. (2009). The Role of Overt Visual Attention in the Process of Perceptual Formation. Rovereto Attention Workshop 2009, Rovereto, Italy
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
Book Chapters
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 [PDF]
Invited Talks (Kietzmann)
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). Reports from our neuroconnectionist frontier: topographies and semantics Cognitive Computational Neuroscience – Keynote, Oxford, UK
Kietzmann, T.C. (2023). chatGPT verstehen. Hintergründe, Chancen, Perspektiven. Sievers World, Osnabrück, Germany
Kietzmann, T.C. (2023). Are categories the right path towards understanding primate vision? IKW Lightning Talks, 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). The neuroconnectionist research programme. GeSiMEx Symposium “Computational Mechanisms in Brains and Machines – Simplicity & Generalizability”, Berlin, Germany
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). 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. University of York seminar, York, UK
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. (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. (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. (2020). It’s about time. Modelling human visual inference with deep recurrent neural networks. NeurIPS – SVRHM Workshop, NeurIPS Conference
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). Neuro-connectionism: how neuro-inspired machine learning leads to insights into human vision. GeSiMEx Workshop (Model-Development in Neuroscience: Generalizability and Simplicity in Mechanistic Explanations), 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. (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 model of visual inference in the brain, Göttingen Neuroscience Seminar, Göttingen University, Göttingen, Germany
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. (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). 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). 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). 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). Understanding vision at the interface of computational neuroscience and artificial intelligence. Computational Cognition Workshop, University of Osnabrück, 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). 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). 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). Understanding vision at the interface of computational neuroscience and machine learning. Birmingham University, Birmingham, UK
Kietzmann, T.C. (2018). Deep learning in cognitive computational neuroscience – a gentle introduction. University of Cambridge, Cambridge, 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 – Seminar Series, University of Victoria, Victoria, Canada
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). 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). 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). 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). Representational dynamics in the human ventral stream captured in recurrent DNNs. Bernstein Conference 2018, Representational Dynamics Workshop, Berlin, Germany
Kietzmann, T.C. (2017). Machine Learning and Representational Similarity Analysis in Cognitive Neuroscience – 2 day workshop. Karolinska Institute, Stockholm, Sweden
Kietzmann, T.C. (2017). Estimating the relative signal- and noise-content of ICA components, Institute of Cognitive Science, University of Osnabrück, Osnabrück, Germany
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
Outreach/Media
Lee, L.W., Kietzmann, J., & Kietzmann, T.C. (2020). Deepfakes: five ways in which they are brilliant business opportunities. The Conversation, UK, Article
Kietzmann, T.C. (2019). What can A.I. and neuroscience learn from each other? Science Night 2019, Cambridge, UK
Kietzmann, T.C. (2019 – today). Member of Skype a Scientist
Kietzmann, T.C. (2018). Life Story. #scientistandparent eLife series (curated by Emma Pewsey et al.), 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)
