Videos from CCN 2019

  • CCN 2019: T-A - Representing states and spaces

  • CCN 2019: T-B - Causal inference

  • CCN 2019: T-C - Approximate inference in the brain: free energy, sampling, and beyond

  • CCN 2019: Opening Remarks (David Sussillo)

  • CCN 2019: Elizabeth Spelke "From core concepts to new systems of knowledge"

  • CCN 2019: Tim Behrens "Abstraction and inference in the frontal hippocampal circuit"

  • CCN 2019: GS-1.1: Learning Divisive Normalization in Primary Visual Cortex

    Max F. Günthner, Santiago A. Cadena, University of Tübingen, Germany; George H. Denfield, Edgar Y. Walker, Baylor College of Medicine, United States; Leon A. Gatys, University of Tübingen, Germany; Andreas S. Tolias, Baylor College of Medicine, United States; Matthias Bethge, Alexander S. Ecker, University of Tübingen, Germany

  • CCN 2019: GS-1.2: Evolving the Olfactory System

    Guangyu Robert Yang, Peter Yiliu Wang, Yi Sun, Ashok Litwin-Kumar, Richard Axel, L.F. Abbott, Columbia University, United States

  • CCN 2019: Roshan Cools "Chemistry of the adaptive mind: on dopamine and mental work"

  • CCN 2019: GS-2.1: An Electrophysiological Signature of Dynamic Urgency in Human Perceptual Decision Making

    Ciara Devine, Trinity College Dublin, Ireland; David McGovern, Dublin City University, Ireland; Jessica Dully, Emmet McNickle, Trinity College Dublin, Ireland; Simon Kelly, University College Dublin, Ireland; Redmond O'Connell, Trinity College Dublin, Ireland

  • CCN 2019: GS-2.2: Model-based value in midbrain dopamine signals

    Marta Blanco Pozo, Thomas Akam, Timothy E. Behrens, Mark E. Walton, University of Oxford, United Kingdom

  • CCN 2019: Anne Churchland "Single-trial neural dynamics during novice and expert decisions are dominated by richly varied movements"

  • CCN 2019: Nando de Freitas "Machine Learning Advances on Imitation"

  • CCN 2019: GS-3.1: Alpha/beta power decreases track the fidelity of stimulus-specific information

    Benjamin J. Griffiths, Stephen D. Mayhew, Karen J. Mullinger, University of Birmingham, United Kingdom; João Jorge, École Polytechnique Fédérale de Lausanne, Switzerland; Ian Charest, Maria Wimber, Simon Hanslmayr, University of Birmingham, United Kingdom

  • CCN 2019: GS-3.2: Automatically inferring task context for continual learning

    Jasmine Collins, Kelvin Xu, Bruno Olshausen, Brian Cheung, University of California Berkeley, United States

  • CCN 2019: Bernhard Schölkopf "Causal learning"

  • CCN 2019: GS-4.1: Self-supervised Neural Network Models of Higher Visual Cortex Development

    Chengxu Zhuang, Stanford University, United States; Siming Yan, Peking University, China; Aran Nayebi, Daniel Yamins, Stanford University, United States

  • CCN 2019: GS-4.2: Why Are Face and Object Processing Segregated in the Human Brain? Testing Computational Hypotheses with Deep Convolutional Neural Networks

    Katharina Dobs, Massachusetts Institute of Technology, United States; Alexander Kell, Columbia University, United States; Ian Palmer, Michael Cohen, Nancy Kanwisher, Massachusetts Institute of Technology, United States

  • CCN 2019: Challenges and Controversies: The Free Energy Principle

  • CCN 2019: Máté Lengyel "Probabilistic internal models — behavioural and neural signatures"

  • CCN 2019: GS-5.1: Functional Decoding using Convolutional Networks on Brain Graphs

    Yu Zhang, Pierre Bellec, Chercheur Centre de recherche de l'institut Universitaire de gériatrie de Montréal (CRIUGM), Canada

  • CCN 2019: GS-5.2: Subnetworks mediating feedforward and feedback processes revealed by multi-area Neuropixels recordings

    Xiaoxuan Jia, Joshua Siegle, Yazan Billeh, Séverine Durand, Greggory Heller, Tamina Ramirez, Anton Arkhipov, Shawn Olsen, Allen Institute, United States

  • CCN 2019: Nathaniel Daw "Population codes and prediction errors"

  • CCN 2019: GS-6.1: Using Inverse Reinforcement Learning to Predict Goal-directed Shifts of Attention

    Gregory Zelinsky, Stony Brook University, United States

  • CCN 2019: GS-6.2: A Memory-Augmented Reinforcement Learning Model of Food Caching Behaviour in Birds

    Johanni Brea, Wulfram Gerstner, EPFL, Switzerland

  • CCN 2019: Talia Konkle "Why is that there? Feature mapping across the visual cortex"

  • CCN 2019: Final Words