Invited seminars and talks

2014

  • pdf Modelling behavioural data
  • 8.9.2014 UCL-MPS Symposium on Computational Psychiatry and Aging, Ringberg Castle, Germany
  • Decision-making in depression
  • 12.6.2014 Computational Psychiatry course, UCL, London, UK
  • Decision-theoretic psychiatry
  • 19.5.2014 Zürich Computational Psychiatry conference, Hospital of Psychiatry, Zürich, Switzerland
  • Computational heuristics in planning
  • 14.3.2014 Institute for Neuroinformatics, University of Zürich, Switzerland
  • Computational heuristics in planning
  • 4.3.2014 Caltech, USA
  • Decision-theoretic psychiatry
  • 28.2.2014 CoSyNe Workshop on Computational Psychiatry, USA
  • Modelling behavioural data
  • 13.2.2014 Zürich SPM Course Tutorial, Zürich, Switzerland
  • 2013

    • pdfReinforcement learning tutorial
    • 25.11.2013 Friedrich Miescher Institute, Switzerland
    • Re-using thoughts - making habits efficient?
    • 25.11.2013 Friedrich Miescher Institute, Switzerland
    • Attributing negative events. A computational tool.
    • 23.10.2013 Computational Psychiatry, Miami, USA
    • Factorial fragmentation and memoization in planning
    • 28.9.2013 Society for Neuroeconomics Annual Conference, Lausanne, Switzerland
    • pdf Reinforcement learning I: theory
    • 14.8.2013 EU Advanced Course on Computational Neuroscience, Bedlewo, Poland
    • pdf Reinforcement learning II: biology
    • 15.8.2012 EU Advanced Course on Computational Neuroscience, Bedlewo, Poland
    • An introduction to computational modelling
    • 26.7.2013 Workshop on Computational Psychopharmacology, British Association of Psychopharmacology Summer Meeting, Harrogate, UK
    • Re-using thoughts - making habits efficient?
    • 18.6.2013 Central European University, Budapest, Hungary
    • Learning processes in the development of addiction
    • 24.5.2013 Dopamine conference, Alghero, Sardinia
    • Re-using thoughts: making habits efficient?
    • 25.4.2013 Berlin Einstein Symposium, Berlin, Germany
    • pdf Modelling behavioural data
    • 13.2.2013 Zürich SPM Course Tutorial, Zürich, Switzerland
    • A generative account of emotional dysfunctions
    • 29.1.2013 Departmental seminar, Universitäre Psychiatrische Dienste, Berne, Switzerland

    2012

    • Defective aversion: using computational methods to dissect decision making in mood disorders
    • 16.1.12 Universitätsklinik für Neurologie, Magdeburg, Germany
    • Mathematizing Madness
    • 27.1.12 Inauguration symposium for Prof. Roshan Cools. Donders Institute, Nijmegen, Netherlands
    • Defective aversion: using computational methods to dissect decision making in mood disorders
    • 14.2.12 Technische Universität Berlin, Germany
    • Defective aversion: the contribution of serotonin
    • 18.6.2012 Devanx final meeting, Paris, France
    • pdf Reinforcement learning I: theory
    • 14.8.2012 EU Advanced Course on Computational Neuroscience, Bedlewo, Poland
    • pdf Reinforcement learning II: biology
    • 15.8.2012 EU Advanced Course on Computational Neuroscience, Bedlewo, Poland
    • Explaining away emotional dysfunctions
    • 14.9.2012 Bernstein Center for Computational Neuroscience annual meeting, München, Germany
    • pdf Modelling behavioural data
    • 17.9.2012 UCL-MPS Symposium on Computational Psychiatry and Aging, Ringberg Castle, Germany
    • Dopamine and learning dysfunctions in addition: a crash course
    • 18.10.2012 Annual meeting of the World Psychiatric Association
    • Computational approaches to the disordered mind
    • 27.11.2012 Departmental Seminar, Institute of biomedical engineering, ETH Zürich, Switzerland

    2011

    2010

    • Affective influences on visual choices.
    • 14.01.10 Rank Prize Meeting in honour of Roger Carpenter: What determines where we look, Grasmere, UK
    • Mapping affective decisions in depression.
    • 27.04.10 Psychiatrische Universitätsklinik, Zürich
    • Using RL tools to map affective decisions in depression.
    • 5.05.10 Wolpert lab, Cambridge University, UK
    • Affective asymmetries. The serotonergic stop signal.
    • 27.05.10 University of Zürich, Switzerland
    • Affective asymmetries. The serotonergic stop signal.
    • 18.06.10 Ecole Normale Superieure, Paris, France

    2009

    • Computational Psychiatry. An application to depression.
    • 5.1.09 Bergman lab, Hebrew University, Israel
    • Behavioural measurements and definitions of anhedonia and helplessness
    • 9.2.09 Charite Hospital, Berlin, Germany
    • A generative model of mood disorders
    • 19.2.09 University of Edinburgh, UK
    • Computational thoughts
    • 30.3.09 Donders Institute, Nijmegen, Netherlands
    • Knowledge and the limits of rationality
    • 28.5.09 Gresham College, UK video
    • RL crash course
    • 20.6.09 University of Magdeburg, Germany. Slides are here

    2008

    • Applying reinforcement learning to mood disorders--an example task.
    • 1.2.08 Gatsby meeting, Center for Theoretical Neuroscience, Columbia University
    • Depression, 5HT and DA: Insights from computational modelling.
    • 21.2.08 Albert Einstein College of Medicine
    • Automatically fitting detailed biophysical models
    • 3.3.08 Comp. Sys. Neurosci. Workshop on Data sharing and modeling challenges in neuroscience
    • Serotonin in reinforcement learning.
    • 26.5.08 Institute Gulbenkian Champalimaud, Portugal
    • Applying reinforcement learning to Depression: a behavioural test
    • 29.5.08 Computational Psychiatry Symposium, IGC, Portugal
    • Applying reinforcement learning to Depression: a behavioural test.
    • 2.6.08 Salzman lab, Columbia University
    • Applying reinforcement learning to Depression: a validation.
    • 12.6.08 Brain Stimulation Division, Columbia University
    • Depression: a computational formulation and a behavioural test.
    • 18.6.08 Department of Neuroscience, NYU
    • Understanding Disorders of the Mind through Neuroimaging: Developing new paradigms.
    • 8.9.08 Wellcome Trust, London

    2007

    • Normative psychiatry.
    • 11.1.07 Neuroeconomics group, UCL
    • Building detailed single-cell models from biophysical data
    • 25.1.07 Michael Häusser lab, UCL
    • Optimal learning: a route to depression?
    • 22.2.07 Comp. Sys. Neurosci. meeting short presentation
    • Depression: attempting a computational dissection.
    • 15.3.07 Functional Imaging Lab, UCL, London
    • Serotonin, inhibition and depression
    • 18.4.07 Cold Spring Harbor Lab
    • Depression -- towards a computational aetiology
    • 28.4.07 NYSPI, Columbia University, New York
    • Parameter inference as a convex problem
    • 25.6.07 EPFL workshop on quantitative neuron models, CH
    • Dopamine: reporting control in depression and mania?
    • 5.9.07 Workshop on Neural bases reward decision making, Institute Gulbenkian Champalimaud, Portugal
    • pdf Serotonin, inhibition and negative moods
    • 5.10.07 Workshop: Theoretical and experimental perspectives on serotonin function, Institute Gulbenkian Champalimaud, Portugal
    • Computational approaches to psychiatry. An application to depression
    • 1.11.07 Symposium: Computational Models in Biological Psychiatry; Computational Cognitive Neuroscience Conference (SfN Satellite);

    2006

    • Fast population coding
    • 15.3.06 Andersen lab, California Institute of Technology, Los Angeles
    • pdf EEG / MEG analysis
    • 28.6.06 Functional Imagning lab, UCL, London
    • pdf Fast population coding
    • 16.7.06 CNS main meeting
    • pdf Inference in stochastic neurones
    • 19.7.06 CNS stochastic dynamics workshop
    • Depression, analgesia and optimality
    • 16.8.06 Max-Planck Institute Tübingen
    • Optimal helplessness. Normative models of depression.
    • 29.8.06 Neurosci. and Psychiatry Unit, Manchester
    • Optimal models of depression.
    • 5.9.06 Maier/Watkins lab,Boulder, Colorado
    • Optimal models of depression.
    • 20.9.06 Mood disorders unit CAMH, Toronto

    2005

    • Fast population coding
    • 5.12.05 Max-Planck Institut für Biologische Kybernetik, Tübingen, Germany
    • Single-cell models
    • 21.11.05 Center for Theoretical Neuroscience, Columbia University, New York
    • Fast population coding
    • 18.11.05 Learning Group, University of Toronto
    • Fast population coding
    • 17.11.05 Becker lab, McMaster University, Hamilton, Canada
    • Efficient infernce of single-cell models
    • 21.10.05 UNIC, CNRS, Gif-sur-Yvette, France
    • Fast population coding
    • 20.10.05 Denève and Gutkin lab, École Normale Superieure, Paris