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Sat Dec 14 08:00 AM -- 06:20 PM (PST) @ East Ballroom A
Real Neurons & Hidden Units: future directions at the intersection of neuroscience and AI
Guillaume Lajoie · Eli Shlizerman · Maximilian Puelma Touzel · Jessica Thompson · Konrad Kording

Workshop Home Page

Recent years have witnessed an explosion of progress in AI. With it, a proliferation of experts and practitioners are pushing the boundaries of the field without regard to the brain. This is in stark contrast with the field's transdisciplinary origins, when interest in designing intelligent algorithms was shared by neuroscientists, psychologists and computer scientists alike. Similar progress has been made in neuroscience where novel experimental techniques now afford unprecedented access to brain activity and function. However, it is unclear how to maximize them to truly advance an end-to-end understanding of biological intelligence. The traditional neuroscience research program, however, lacks frameworks to truly advance an end-to-end understanding of biological intelligence. For the first time, mechanistic discoveries emerging from deep learning, reinforcement learning and other AI fields may be able to steer fundamental neuroscience research in ways beyond standard uses of machine learning for modelling and data analysis. For example, successful training algorithms in artificial networks, developed without biological constraints, can motivate research questions and hypotheses about the brain. Conversely, a deeper understanding of brain computations at the level of large neural populations may help shape future directions in AI. This workshop aims to address this novel situation by building on existing AI-Neuro relationships but, crucially, outline new directions for artificial systems and next-generation neuroscience experiments. We invite contributions concerned with the modern intersection between neuroscience and AI and in particular, addressing questions that can only now be tackled due to recent progress in AI on the role of recurrent dynamics, inductive biases to guide learning, global versus local learning rules, and interpretability of network activity. This workshop will promote discussion and showcase diverse perspectives on these open questions.

Opening Remarks (announcements)
Invited Talk: Hierarchical Reinforcement Learning: Computational Advances and Neuroscience Connections (talk)
Invited Talk: Deep learning without weight transport (talk)
Contributed talk: Eligibility traces provide a data-inspired alternative to backpropagation through time. Guillaume Bellec, Franz Scherr, Elias Hajek, Darjan Salaj, Anand Subramoney, Robert Legenstein, Wolfgang Maass (talk)
Coffee Break + Posters (break)
Invited Talk: Computing and learning in the presence of neural noise (talk)
Invited Talk: Universality and individuality in neural dynamics across large populations of recurrent networks (talk)
Contributed talk: How well do deep neural networks trained on object recognition characterize the mouse visual system? Santiago A. Cadena, Fabian H. Sinz, Taliah Muhammad, Emmanouil Froudarakis, Erick Cobos, Edgar Y. Walker, Jake Reimer, Matthias Bethge, (talk)
Contributed talk: Functional Annotation of Human Cognitive States using Graph Convolution Networks Yu Zhang, Pierre Bellec (talk)
Lunch Break (break)
Invited Talk: Simultaneous rigidity and flexibility through modularity in cognitive maps for navigation (talk)
Invited Talk: Theories for the emergence of internal representations in neural networks: from perception to navigation (talk)
Contributed talk: Adversarial Training of Neural Encoding Models on Population Spike Trains Poornima Ramesh, Mohamad Atayi, Jakob H Macke (talk)
Contributed talk: Learning to Learn with Feedback and Local Plasticity. Jack Lindsey (talk)
Coffee Break + Posters (break)
Poster Session (posters)
Invited Talk: Sensory prediction error signals in the neocortex (talk)
Panel Session: A new hope for neuroscience (panel)