Speakers > Richard Hahnloser
ETH Zurich, Zurich, Switzerland Richard Hahnloser studied Physics and is Professor and Co-director of the Institute of Neuroinformatics, jointly affiliated with the University of Zurich and ETH Zurich. He studies vocal production and vocal learning in songbirds and is interested in sensory and motor systems and how they interact to solve a computational task. He is affiliated with the Neuroscience Center Zurich (ZNZ) and with the Swiss National Centre of Competence in Research (NCCR) Evolving Language. Reinforcement learning of birdsong Reinforcement learning (RL) is a promising computational theory for understanding learned skilled behavior and its underlying neural mechanisms. Yet, most natural behaviors are too variable to be compatible with optimal action policies that strictly maximize reward. To reconcile these views, we postulate that optimality in reinforcement learning applies not to behavior itself but to a latent source of motor variability, from which an animal learns. We assume that this source is an ideal source of randomness, all other aspects of behavioral variability we attribute to non-ideal variability sources from which an animal cannot directly learn.
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