_{Publikationen:
Abteilung für Computational Neuroscience}

Brielmann, A.; Nuzzo, A.; Pelli, D.: Beauty, the feeling. Acta Psychologica 219, S. 1 - 12 (2021)

Terenzi, D.; Liu, L.; Bellucci, G.; Park, S.: Determinants and modulators of human social decisions. Neuroscience and Biobehavioral Reviews 128, S. 383 - 393 (2021)

Mancinelli, F.; Roiser, J.; Dayan, P.: Internality and the internalisation of failure: Evidence from a novel task. PLoS Computational Biology 17 (7), S. 1 - 25 (2021)

Moran, R.; Dayan, P.; Dolan, R.: Efficiency and prioritization of inference-based credit assignment. Current Biology 31 (13), S. 2747 - 2756 (2021)

Kóbor, A.; Kardos, Z.; Takács, Á.; Éltetö, N.; Janacsek, K.; Tóth-Fáber, E.; Csépe, V.; Nemeth, D.: Adaptation to recent outcomes attenuates the lasting effect of initial experience on risky decisions. Scientific Reports 11 (1), 10132, S. 1 - 20 (2021)

Brielmann, A.; Pelli, D.: The pleasure of multiple images. Attention, Perception & Psychophysics 83 (3), S. 1179 - 1188 (2021)

Brielmann, A.; Pelli, D.: Correction to: The pleasure of multiple images. Attention, Perception & Psychophysics 83 (3), S. 1189 (2021)

Dayan, P.: When will's wont wants wanting. Behavioral and Brain Sciences 44, e35 (2021)

Faulkner, P.; Huys, Q.; Renz, D.; Eshel, N.; Pilling, S.; Dayan, P.; Roiser, J.: A Comparison of "Pruning" During Multi-Step Planning in Depressed and Healthy Individuals. Psychological Medicine Epub ahead (2021)

Sargent, K.; Chavez-Baldini, U.; Master, S.; Verweij, K.; Lok, A.; Sutterland, A.; Vulink, N.; Denys, D.; Smit, D.; Nieman, D.: Resting-state brain oscillations predict cognitive function in psychiatric disorders: A transdiagnostic machine learning approach. NeuroImage: Clinical 30, 102617, S. 1 - 9 (2021)

Sukenik, N.; Vinogradov, O.; Weinreb, E.; Segal, M.; Levina, A.; Moses, E.: Neuronal circuits overcome imbalance in excitation and inhibition by adjusting connection numbers. Proceedings of the National Academy of Sciences of the United States of America 118 (12), S. 1 - 9 (2021)

Akam, T.; Rodrigues-Vaz, I.; Marcelo , I.; Zhang, X.; Pereira, M.; Freire Oliveira , R.; Dayan, P.; Costa, R.: The Anterior Cingulate Cortex Predicts Future States to Mediate Model-Based Action Selection. Neuron 109 (1), S. 149 - 163 (2021)

Hall-McMaster, S.; Dayan, P.; Schuck, N.: Control over patch encounters changes foraging behaviour. iScience Epub ahead (2021)

Moran, R.; Dayan, P.; Dolan, R.: Human subjects exploit a cognitive map for credit assignment. Proceedings of the National Academy of Sciences of the United States of America 118 (4), S. 1 - 12 (2021)

Neville, V.; Dayan, P.; Gilchrist, I.; Paul, E.; Mendl, M.: Dissecting the links between reward and loss, decision-making, and self-reported affect using a computational approach. PLoS Computational Biology 17 (1), S. 1 - 27 (2021)

Bröker, F.; Ramscar, M.: Representing absence of evidence: why algorithms and representations matter in models of language and cognition. Language, Cognition and Neuroscience Epub ahead (2020)

Gagne, C.; Zika, O.; Dayan, P.; Bishop, S.: Impaired adaptation of learning to contingency volatility in internalizing psychopathology. eLife 9, S. 1 - 51 (2020)

Schulz, E.; Dayan, P.: Computational psychiatry for computers. iScience 23 (12), 101772, S. 1 - 9 (2020)

Schulz, L.; Rollwage, M.; Dolan, R.; Fleming, S.: Dogmatism manifests in lowered information search under uncertainty. Proceedings of the National Academy of Sciences of the United States of America 117 (49), S. 31527 - 31534 (2020)

Bellucci, G.; Camilleri, J.; Eickhoff, S.; Krueger, F.: Neural signatures of prosocial behaviors. Neuroscience and Biobehavioral Reviews 118, S. 186 - 195 (2020)

Dayan, P.: Learning rules. Current Biology 30 (21), S. R1289 - R1290 (2020)

Dezfouli, A.; Nock, R.; Dayan, P.: Adversarial vulnerabilities of human decision-making. Proceedings of the National Academy of Sciences of the United States of America 117 (46), 202016921, S. 29221 - 29228 (2020)

Bellucci, G.: Positive attitudes and negative expectations in lonely individuals. Scientific Reports 10 (1), 18595, S. 1 - 9 (2020)

Kastner, D.; Gillespie, A.; Dayan, P.; Frank, L.: Memory Alone Does Not Account for the Way Rats Learn a Simple Spatial Alternation Task. The Journal of Neuroscience 40 (38), S. 7311 - 7317 (2020)

Neville, V.; King, J.; Gilchrist, I.; Dayan, P.; Paul, E.; Mendl, M.: Reward and punisher experience alter rodent decision-making in a judgement bias task. Scientific Reports 10 (1), 11839, S. 1 - 14 (2020)

Bellucci, G.; Camilleri, J.; Iyengar, V.; Eickhoff, S.; Krueger, F.: The Emerging Neuroscience of Social Punishment: Meta-Analytic Evidence. Neuroscience and Biobehavioral Reviews 113, S. 426 - 439 (2020)

Bellucci, G.; Münte, T.; Park , S.: Effects of a dopamine agonist on trusting behaviors in females. Psychopharmacology 237 (6), S. 1671 - 1680 (2020)

Dayan, P.: Representation, abstraction, and simple-minded sophisticates. Behavioral and Brain Sciences 43, e126 (2020)

Eldar, E.; Lièvre, G.; Dayan, P.; Dolan, R.: The roles of online and offline replay in planning. eLife 9, e56911, S. 1 - 23 (2020)

Iigaya, K.; Hauser, T.; Kurth-Nelson, Z.; O'Doherty, J.; Dayan, P.; Dolan, R.: The value of what’s to come: Neural mechanisms coupling prediction error and the utility of anticipation. Science Advances 6, 25, S. 1 - 16 (2020)

Iigaya, K.; Hauser, T.; Kurth-Nelson, Z.; O'Doherty, J.; Dayan, P.; Dolan , R.: The Value of What's to Come: Neural Mechanisms Coupling Prediction Error and the Utility of Anticipation. Science Advances 6 (25), eaba3828, S. 1 - 16 (2020)

Krueger, F.; Bellucci, G.; Xu, P.; Feng, C.: The Critical Role of the Right Dorsal and Ventral Anterior Insula in Reciprocity: Evidence From the Trust and Ultimatum Games. Frontiers in Human Neuroscience 14, 176, S. 1 - 6 (2020)

Mobbs, D.; Headley, D.; Ding, W.; Dayan, P.: Space, Time, and Fear: Survival Computations along Defensive Circuits. Trends in Cognitive Sciences 24 (3), S. 228 - 241 (2020)

Schad, D.; Rapp, M.; Garbusow, M.; Nebe, S.; Sebold, M.; Obst, E.; Sommer, C.; Deserno, L.; Rabovsky, M.; Friedel, E. et al.; Romanczuk-Seiferth , N.; Wittchen, H.-U.; Zimmermann, U.; Walter, H.; Sterzer, P.; Smolka, M.; Schlagenhauf, F.; Heinz, A.; Dayan, P.; Huys, Q.: Dissociating neural learning signals in human sign- and goal-trackers. Nature Human Behaviour 4 (2), S. 201 - 214 (2020)

Stojić, H.; Orquin, J.; Dayan, P.; Dolan, R.; Speekenbrink, M.: Uncertainty in learning, choice and visual fixation. Proceedings of the National Academy of Sciences of the United States of America 117 (6), S. 3291 - 3300 (2020)

Lloyd, K.; Sanborn, A.; Leslie, D.; Lewandowsky, S.: Why Higher Working Memory Capacity May Help You Learn: Sampling, Search, and Degrees of Approximation. Cognitive Science 43 (12), S. 1 - 43 (2019)

Wise, T.; Michely, J.; Dayan, P.; Dolan, R.: A computational account of threat-related attentional bias. PLoS Computational Biology 15 (10), S. 1 - 21 (2019)

Zhao, S.; Chait, M.; Dick, F.; Dayan, P.; Furukawa, S.; Liao, H.: Pupil-linked phasic arousal evoked by violation but not emergence of regularity within rapid sound sequences. Nature Communications 10, 4030, S. 1 - 16 (2019)

Javadi, A.; Patai, E.; Marin-Garcia, E.; Margolis, A.; Tan, H.-R.; Kumaran, D.; Nardini, M.; Duzel, E.; Dayan, P.; Spiers, H.: Prefrontal Dynamics Associated with Efficient Detours and Shortcuts: A Combined Functional Magnetic Resonance Imaging and Magnetoencenphalography Study. Journal of Cognitive Neuroscience 31 (8), S. 1227 - 1247 (2019)

Na, S.; Chung, D.; Jung, J.; Hula, A.; Fiore, V.; Dayan, P.; Gu, X.: Humans Use Forward Thinking to Exert Social Control. Neuron (angenommen)

Javadi, A.-H.; Patai, E.; Marin-Garcia, E.; Margois, A.; Tan, H.-R.; Kumaran, D.; Nardini, M.; Penny, W.; Duzel, E.; Dayan, P. et al.; Spiers, H.: Backtracking during navigation is correlated with enhanced anterior cingulate activity and suppression of alpha oscillations and the "default-mode" network. Proceedings of the Royal Society B: Biological Sciences 286 (1908), 20191016, S. 1 - 9 (2019)

Ahilan, S.; Solomon, R.; Breton, A.-Y.; Conover, K.; Niyogi, R.; Shizgal, P.; Dayan, P.: Learning to use past evidence in a sophisticated world model. PLoS Computational Biology 15 (6), S. 1 - 20 (2019)

Dezfouli , A.; Griffiths, K.; Ramos, F.; Dayan, P.; Balleine, B.: Models that learn how humans learn: The case of decision-making and its disorders. PLoS Computational Biology 16 (6), S. 1 - 33 (2019)

Rouault, M.; Dayan, P.; Fleming, S.: Forming global estimates of self-performance from local confidence. Nature Communications 10 (1), 1141, S. 1 - 11 (2019)

Moran, R.; Keramati, M.; Dayan, P.; Dolan, R.: Retrospective model-based inference guides model-free credit assignment. Nature Communications 10, 750, S. 1 - 14 (2019)

Moutoussis, M.; Bullmore, E.; Goodyer, I.; Fonagy, P.; Jones, P.; Dolan, R.; Dayan, P.: Change, stability, and instability in the Pavlovian guidance of behaviour from adolescence to young adulthood. PLoS Computational Biology 14 (12), S. 1 - 26 (2018)

Brielmann, A.: Empirical Aesthetics. In: Internet Encyclopedia of Philosophy (Hg. Fieser, J.; Dowden, B.) (2021)

Gouw, M.; Alvarado-Valverde, J.; Čalyševa, J.; Diella, F.; Kumar, M.; Michael, S.; Van Roey, K.; Dinkel, H.; Gibson, T.: How to Annotate and Submit a Short Linear Motif to the Eukaryotic Linear Motif Resource. In: Intrinsically Disordered Proteins: Methods and Protocols, S. 73 - 102 (Hg. Kragelund, B.; Skriver, K.). Humana, New York, NY, USA (2020)

Dayan, P.; Nakahara, H.: Models and Methods for Reinforcement Learning. In: Stevens' Handbook of Experimental Psychology and Cognitive Neuroscience, Bd. 5: Methodology, 4. Aufl., S. 1 - 40 (Hg. Wixted, J.; Wagenmakers, E.-J.). Wiley, Hoboken, NJ, USA (2018)

Khajehabdollahi, S.; Giannakakis, E.; Prosi, J.; Levina, A.: Reservoir computing with self-organizing neural oscillators. In: Artificial Life Conference Proceedings, Bd. 2021, 78, S. 1 - 3. ALIFE 2021: The 2021 Conference on Artificial Life, Praha, Czech Republic, 19. Juli 2021 - 23. Juli 2021. MIT Press (2021)

Prosi, J.; Khajehabdollahi, S.; Giannakakis, E.; Martius, G.; Levina, A.: The dynamical regime and its importance for evolvability, task performance and generalization. In: Artificial Life Conference Proceedings, Bd. 2021, 79, S. 1 - 9. ALIFE 2021: The 2021 Conference on Artificial Life, Praha, Czech Republic, 19. Juli 2021 - 23. Juli 2021. MIT Press (2021)

Tano, P.; Dayan, P.; Pouget, A.: A local temporal difference code for distributional reinforcement learning. In: Advances in Neural Information Processing Systems 33, S. 13662 - 13673 (Hg. Larochelle, H.; Ranzato, M.; Hadsell, R.; Balcan, M.-F.; Lin, H.-T.). 34th Conference on Neural Information Processing Systems (NeurIPS 2020), 06. Dezember 2020 - 12. Dezember 2020. Curran, Red Hook, NY, USA (2021)

Xia, L.; Master, S.; Eckstein, M.; Wilbrecht, L.; Collins, A.: Learning under uncertainty changes during adolescence. In: 42nd Annual Meeting of the Cognitive Science Society (CogSci 2020): 5Developing a Mind: Learning in Humans, Animals, and Machines, S. 716 - 722 (Hg. Denison, S.; Mack, M.; Xu, Y.; Armstrong, B.). 42nd Annual Virtual Meeting of the Cognitive Science Society (CogSci 2020), Toronto, Canada, 29. Juli 2020 - 01. August 2020. Curran, Red Hook, NY, USA (2020)

Ahilan, S.; Dayan, P.: Correcting Experience Replay for Multi-Agent Communication. In: Ninth International Conference on Learning Representations (ICLR 2021). Ninth International Conference on Learning Representations (ICLR 2021), Vienna, Austria, 03. Mai 2021 - 07. Mai 2021. (eingereicht)

Sezener, E.; Dayan, P.: Static and Dynamic Values of Computation in MCTS. 36th Conference on Uncertainty in Artificial Intelligence (UAI 2020), 03. August 2020 - 06. August 2020. Proceedings of Machine Learning Research (PMLR) 124, 26, S. 31 - 40 (2020)

Browning, M.; Carter, C.; Chatham, C.; Den Ouden, H.; Gillan, C.; Baker, J.; Chekroud, A.; Cools, R.; Dayan, P.; Gold, J. et al.; Goldstein, R.; Hartley, C.; Kepecs, A.; Lawson, R.; Mourao-Miranda, J.; Phillips, M.; Pizzagalli, D.; Powers, A.; Rindskopf, D.; Roiser, J.; Schmack, K.; Schiller, D.; Sebold, M.; Stephan, K.; Frank, M.; Huys, Q.; Paulus, M.: Realizing the Clinical Potential of Computational Psychiatry: Report from the Banbury Center Meeting, February 2019. Banbury Center Meeting 2019, Huntington, NY, USA, 2019-02. Biological Psychiatry 88 (2), S. e5 - e10 (2020)

Dezfouli, A.; Ashtiani, H.; Ghattas, O.; Nock, R.; Dayan, P.; Ong, C.: Disentangled behavioural representations. In: Advances in Neural Information Processing Systems 32, S. 2243 - 2252 (Hg. Wallach, H.; Larochelle, H.; Beygelzimer , A.; d'Alché-Buc, F.; Fox, E. et al.). Thirty-third Conference on Neural Information Processing Systems (NeurIPS 2019), Vancouver, Canada, 09. Dezember 2019 - 13. Dezember 2019. Curran, Red Hook, NY, USA (2020)

Jain, Y.; Gupta, S.; Rakesh, V.; Dayan, P.; Callaway, F.; Lieder, F.: How do people learn how to plan? In: Conference on Cognitive Computational Neuroscience (CCN 2019), PS-2A.70, S. 826 - 829. Conference on Cognitive Computational Neuroscience (CCN 2019), Berlin, Germany, 13. September 2019 - 16. September 2019. (2019)

Ahilan, S.; Dayan, P.: Feudal Multi-Agent Hierarchies for Cooperative Reinforcement Learning. In: Annual Conference of the American Library Association (ALA 2019), 5, S. 1 - 5. Annual Conference of the American Library Association (ALA 2019) , Washington, DC, USA, 20. Juni 2019 - 25. Juni 2019. (2019)

Ahilan, S.; Dayan, P.: Feudal Multi-Agent Hierarchies for Cooperative Reinforcement Learning. In: Workshop on Structure & Priors in Reinforcement Learning (SPiRL 2019) at ICLR 2019, S. 1 - 11. Workshop on Structure & Priors in Reinforcement Learning (SPiRL 2019) at ICLR 2019, New Orleans, LA, USA, 06. Mai 2019. (2019)

Deserno, L.; Moran, R.; Lee, Y.; Michely, J.; Dayan, P.; Dolan, R.: Dopamine enhances model-free credit assignment through boosting of retrospective model-based inference. In Biological Psychiatry, 89 (9 Supplement), S. S94. 2021 Annual Scientific Convention and Meeting of the Society of Biological Psychiatry (SOBP 2021), 29. April 2021 - 01. Mai 2021. Elsevier, New York (2021)

Dayan, P.: Impact beyond neuroscience (translational non-clinical). In FENS 2020 Virtual Forum, 1522. FENS 2020 Virtual Forum, Glasgow, UK, 11. Juli 2020 - 15. Juli 2020. (2020)

Rouault, C.; Dayan, P.; Fleming, S.: Forming global estimates of self-performance from local confidence. In 61st Conference of Experimental Psychologists: 61. TeaP 2019 (Tagung Experimentell Arbeitender PsychologInnen) (Hg. Lange-Küttner, C.). 61st Conference of Experimental Psychologists (TEAP 2019), London, UK, 15. April 2019 - 17. April 2019. Pabst, Lengerich, Germany (2020)

Dayan, P.; Iigaya, K.; Hauser, T.; Kurth-Nelson, Z.; O'Doherty, J.; Dolan, R.: Anticipation, imagination and information seeking via mid-brain, hippocampal, and prefrontal interactions. In 49th Annual Meeting of the Society for Neuroscience (Neuroscience 2019), 018.12, S. 103 - 104. 49th Annual Meeting of the Society for Neuroscience (Neuroscience 2019), Chicago, IL, USA, 19. Oktober 2019 - 23. Oktober 2019. (2019)

Mendl, M.; Jones, S.; Neville, V.; Higgs, L.; Robinson, E.; Dayan, P.; Paul, E.: An automated and self-initiated judgement bias task based on natural investigative behaviour. In Applied Ethology 2019: Animal lives worth living: 53rd Congress of the International Society of Applied Ethology (ISAE 2019), S. 126 (Hg. Newberry, R.; Braastad, B.). 53rd Congress of the International Society of Applied Ethology (ISAE 2019), Bergen, Norway, 05. August 2019 - 09. August 2019. Wageningen Academic Publishers, Wageningen, The Netherlands (2019)

Neville, V.; Paul, L.; Dayan, P.; Gilchrist, I.; Mendl, M.: Investigating animal affect and welfare using computational modelling. In Applied Ethology 2019: Animal lives worth living: 53rd Congress of the International Society of Applied Ethology (ISAE 2019), S. 127 (Hg. Newberry, R.; Braastad, B.). 53rd Congress of the International Society of Applied Ethology (ISAE 2019), Bergen, Norway, 05. August 2019 - 09. August 2019. Wageningen Academic Publishers, Wageningen, The Netherlands (2019)

Vilares, I.; Nolte, T.; Hula, A.; Cui, Z.; Fonagy, P.; Zhu, L.; Chiu, P.; King-Casas, B.; Lohrenz, T.; Dayan, P. et al.; Montague, R.: Computational Phenotyping in Borderline Personality Using a Role-Based Social Hierarchy Probe. In Biological Psychiatry, 85 (10 Supplement), O8, S. S108. 74th Annual Meeting of the Society of Biological Psychiatry, Chicago, IL, USA, 16. Mai 2019 - 18. Mai 2019. Elsevier, New York (2019)

Buendia, V.: Jensen’s force and the origin of asynchronous irregular states. International Conference on Mathematical Neuroscience (ICMNS 2021) (2021)

Dayan, P.: Peril, Prudence and Planning as Risk, Avoidance and Worry. Berlin School of Mind and Brain: SCIoI Distinguished Speaker Series , Berlin, Germany (2021)

Dayan, P.: Peril, Prudence and Planning as Risk, Avoidance and Worry. California Institute of Technology: T&C Chen Center for Social Decision Neuroscience Seminar, Pasadena, CA, USA (2021)

Dinkel, H.: SoftwareCarpentry: Extrakurrikuläre Weiterbildung für den Forscher von heute. FDM-Kompetenzen: Forschungsdaten an den Max-Planck-Instituten (2021)

Dayan, P.: Peril, Prudence and Planning as Risk, Avoidance and Worry. The Leslie and Susan Gonda (Goldschmied) Multidisciplinary Brain Research Center: Bar-Ilan University, Ramat Gan, Israel (2021)

Dayan, P.: Neural Reinforcement Learning. Brain Awareness Week: Webinar Series Regional Centre for Biotechnology, Haryana, India (2021)

Dayan, P.: Peril, Prudence and Planning as Risk, Avoidance and Worry. Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands (2021)

Dayan, P.: Modelling and Manipulating Behaviour Using Recurrent Networks. Donders Institute for Brain, Cognition and Behaviour: Donders lectures, Nijmegen, The Netherlands (2021)

Dayan, P.; Gagne, C.: Peril, Prudence and Planning as Risk, Avoidance and Worry. The Fields Institute for Research in Mathematical Sciences: 2020-2021 Machine Learning Advances and Applications Seminar, Toronto, Canada (2021)

Dayan, P.: Replay and Preplay in Human Planning. Jülich Forschungszentrum: Institute of Neuroscience and Medicine, Computational and Systems Neuroscience (INM-6 / IAS-6), Jülich, Germany (2020)

Dinkel, H.: Version Control with Git & GitHub. EMBL Course: Computing Skills For Reproducible Research: Software Carpentry - Virtual (2020)

Dayan, P.: Replay in Offline Planning. Pattern Recognition in Neuroimaging Summer School 2020 (PRNI), Wien, Austria (2020)

Dayan, P.: Replay. 43th Annual Meeting of the Japan Neuroscience Society, Kobe, Japan (2020)

Dayan, P.: Modelling and Manipulating Behaviour Using Recurrent Networks. Machine Learning in Science: Virtual Cluster Conference, Tübingen, Germany (2020)

Dayan, P.: Model based and model free interactions: inference, influence and confidence. 2020 Intelligent Systems Summer Colloquium, Tübingen, Germany (2020)

Laurent, G.; Yaksi, E.; Lüscher, C.; Ming, G.-L.; Roberts, A.; Dayan, P.; Bale, T.; Tracey, I.: The Brain Debate. FENS 2020 Virtual Forum, Glasgow, UK (2020)

Dayan, P.: Machine learning for Computational Neuroscience. Machine Learning Summer School (MLSS 2020), Tübingen, Germany (2020)

Dayan, P.: All Models Are Wronged. Gordon Research Conference: Functions of the Basal Ganglia from Thought to Action , Ventura, CA, USA (2020)

Dayan, P.; Iigaya, K.; Hauser, T.; Kurth-Nelson, Z.; O'Doherty, J.; Dolan, R.: Savouring and its Modulation by Prediction Errors. Departmental Colloquium Max Planck for Biological Cybernetics, Tübingen, Germany (2019)

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)

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)

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)

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)

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)