Bernstein Conference 2022
TU Berlin
Date
Sep 13 – 16
Abstracts
Keynote Lecture
Sonja Hofer | University College London, UK
Making sense of what you see: cortical and thalamic circuits for vision
Invited Talks
Bing Brunton | University of Washington, USA
Tracking turbulent plumes with deep reinforcement learning
Christine Constantinople | New York University, USA
Distinct controllers for motivation and deliberation
Carina Curto | Pennsylvania State University, USA
Sequences and modularity of dynamic attractors in inhibition-dominated neural networks
Liset M de la Prida | Instituto Cajal, Spain
Understanding hippocampal activities using machine learning and data science tools
Juan Alvaro Gallego | Imperial College London, UK
Understanding the emergence of neural population dynamics underlying behaviour
Mehrdad Jazayeri | Massachusetts Institute of Technology, USA
Timing via counting using attractor networks in the entorhinal cortex
Gaby Maimon | The Rockefeller University, New York, USA
How brains add vectors
Andrew Saxe | University College London, UK
Why learn representations? Abstraction and generalization in a nonlinear deep network
Henning Sprekeler | Technische Universität Berlin, Germany
Top-down models of inhibitory circuits
Carsen Stringer | Janelia Research Campus, USA
Uncovering features of high-dimensional neural and behavioral data
Brains for Brains Young Researcher Award Winner
Simone Azeglio | Institut de l’Audition, Institut Pasteur, France
Activity-driven deep models for learning sound transformations across the auditory pathway
Contributed Talks
David Dahmen | Forschungszentrum Jülich, Germany
Strong recurrency of cortical networks constrains activity in low-dimensional subspaces
Paul Haider | University of Bern, Switzerland
Latent Equilibrium: A unified learning theory for arbitrarily fast computation with arbitrarily slow neurons
Kanghoon Jung | Johns Hopkins University, USA
Dopamine-mediated cellular programming of heuristic decisions
Ioannis Pisokas | University of Edinburgh, UK
How ants remember their way home
Aviv Ratzon | Technion, Israel Institute of Technology, Israel
Representational Drift As a Result of Implicit Regularization
Hazem Toutounji | University of Nottingham, UK
Selective Attention Aids Rapid Learning in Complex Environments
Sigrid Trägenap | Frankfurt Institute of Advanced Studies, Germany
Experience drives the development of novel, reliable cortical sensory representations from endogenous networks
Ivan Voitov | Sainsbury Wellcome Centre, UK
Cortical feedback loops bind distributed high-dimensional representations of working memory
Katharina Wilmes | University of Bern, Switzerland
Uncertainty-modulated prediction errors in cortical microcircuits
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