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Dinkel, H.: Introduction of Linear motifs. EMBO Practical Course: Computational analysis of protein-protein interactions in cell function and disease, Bangalore, India (2019)

Dinkel, H.; Gibson, T.; Kumar, M.: Linear motif biology and prediction, cooperativity in cellular signaling (I). EMBO Practical Course: Computational analysis of protein-protein interactions in cell function and disease, Bangalore, India (2019)

Higgins, I.; Konkle, T.; Bethge, M.: What sorts of cognitive or biological (architectural) inductive biases will be crucial for developing effective artificial intelligence? NeurIPS 2019 Workshop: Shared Visual Representations in Human and Machine Intelligence, Vancouver, BC, Canada (2019)

Kumar, M.; Dinkel, H.; Gibson, T.: Linear motif biology and prediction, cooperativity in cellular signaling (II). EMBO Practical Course: Computational analysis of protein-protein interactions in cell function and disease, Bangalore, India (2019)

Lorenz, R.: Neuroadaptive Bayesian optimization in cognitive neuroscience. BrainModes 2019, Pokhara, Nepal (2019)

Safavi, S.; Logothetis, N.; Besserve, M.: Multivariate coupling estimation between continuous signals and point processes. NeurIPS 2019 Workshop: Learning with Temporal Point Processes, Vancouver, Canada (2019)

Sharan, M.; Dinkel, H.: Using informal meetings for building local (bioinformatics) communities. EMBO Practical Course: Computational analysis of protein-protein interactions in cell function and disease, Bangalore, India (2019)

Dayan, P.: Replay and Preplay in Human Planning. Université de Genève: C. Lüscher Synapses, Circuits and Behaviour in Addiction and Related Disorders, Genève, Switzerland (2019)

Dayan, P.: Neural Reinforcement Learning. Humboldt Lecture Series at Tübingen University , Tübingen, Germany (2019)

Dayan, P.; Iigaya, K.; Hauser, T.; Kurth-Nelson, Z.; O'Doherty, J.; Dolan, R.: Savouring and its Modulation by Prediction Errors. Colloquium Cognitive Systems: Universität Ulm , Ulm, Germany (2019)

Foster, C.: Decoding body subcategories from human brain activity. Face Perception Research Lab, School of Behavioral and Brain Sciences, The University of Texas, Austin, TX, USA (2019)

Hagberg, G.: Assessing brain microstructure in vivo through quantitative MRI at ultrahigh magnetic fields: hurdles and opportunities. Università degli Studi di Trieste: DSV and NeSC Doctoral Seminars, Trieste, Iraly (2019)

Dayan, P.: Savouring and its Modulation by Prediction Errors. 17th Annual Meeting of the Society for NeuroEconomics (SNE 2019), Dublin, Ireland (2019)

Dayan, P.: Theoretical Neuroscience: Decision Making and its Discontents. 49th Annual Meeting of the Society for Neuroscience (Neuroscience 2019) , Chicago, IL, USA (2019)

Lohmann, G.: Statistical inference for fMRI at 3T and beyond. Max Planck Institute for Biological Cybernetics, Tübingen, Germany (2019)

Sharan, M.; Dinkel, H.: Building Blocks of Python Programming. EMBL Course: Computing Skills For Reproducible Research: Software Carpentry, Heidelberg, Germany (2019)

Sharan, M.; Dinkel, H.; Alves, R.: Building programs with Python I. EMBL Course: Computing Skills For Reproducible Research: Software Carpentry, Heidelberg, Germany (2019)

Sharan, M.; Dinkel, H.; Alves, R.: Building programs with Python II. EMBL Course: Computing Skills For Reproducible Research: Software Carpentry, Heidelberg, Germany (2019)

Sharan, M.; Dinkel, H.; Alves, R.: Using commandline git for version control. EMBL Course: Computing Skills For Reproducible Research: Software Carpentry, Heidelberg, Germany (2019)

Besserve, M.: Can we help the brain rewire itself? Hints from the functions and mechanisms acting during sleep. Data, Learning and Inference (DALI 2019), San Sebastian, Spain (2019)