5th Bernstein Sparks Workshop: Neural models of decision making in natural inference tasks - from theory to experiment June 11-12, 2015 in Tübingen, Germany Abstract submission and registration deadline: May 20, 2015 (attendance limited: first come, first served) Detailed information at http://www.bccn-tuebingen.de/events/5th-bernstein-sparks-workshop-2015.html Confirmed Speakers: • Wieland Brendel (University of Tuebingen) • David Cox (Harvard University) • Jaime de la Rocha (IDIBAPS, Barcelona) • Nicolas Heess (Google DeepMind) • Mate Lengyel (University of Cambridge) • Wolfgang Maass (Technical University Graz) • Hendrikje Nienborg (University of Tuebingen) • Andreas Tolias (BCM Houston) • Dan Yamins (MIT) • Rich Zemel (University of Toronto) The brain needs to extract behaviorally relevant information from sensory signals which contain only indirect, incomplete and highly variable information about the world. Beginning with the study of the neural basis of random dot motion discrimination in the late eighties, neural decision making has been studied extensively in neuroscience. However, the models used to reason about decision making do not account for the computational complexity of natural inference tasks such as object recognition, speech recognition or alike. Based on recent advances in machine learning more and more complex artificial neural networks are developed that become increasingly proficient in mimicking perceptual inference abilities of humans and animals. As a side effect of their popularity in technology, the increasing availability and diversity of high-performing neural network models opens a new door for studying the neural mechanisms of robust decision making. Important differences between these networks are the presence or absence of feedback connections, the presence or absence of stochasticity, and the diversity of different nonlinear mechanisms. The existence of this diversity mirrors important discussions in neuroscience on the role and effect of feedback signals is in the brain [1], whether the brain represents and computes with probabilities [2], whether feedback signals are essential for performing probabilistic inference in hierarchical models [3], and whether neural stochasticity can be interpreted in terms of sampling [4] or regularization such as dropout [5]. As a joint effort between theoreticians and experimentalists, the goal of this Sparks workshop will be to survey and discuss the role of these mechanisms for robust decision making in artificial and real neural networks and to derive discriminative experimental tests and tools that seem most promising to analyze them. [1] Gilbert & Li, Nat Rev Neuro 2013 [2] Pouget et al, Nat Rev Neuro 2013 [3] Lee & Mumford, JOSA 2003 [4] Fiser et al., TICS 2010 [5] Srivastava et al, JMLR 2014 Organizers: • Matthias Bethge (BCCN Tübingen) • Ralf Haefner (University of Rochester) • Richard Hahnloser (University of Zürich) • Bernstein Coordination Site (BCOS)