Real Neurons & Hidden Units:

Future directions at the intersection of neuroscience and artificial intelligence @ NeurIPS 2019

Neuroscience and AI have a long and entangled history, and modern progress in both fields draws on one another. However, the recent explosion of AI and the development of evermore powerful experimental methods in neuroscience invite us to revisit the extent to which brains and machines can work the same way. Will understanding how the brain works really lead us to build better AI? Will building better AI really help us understand how the brain works? This workshop intends to address these contentious topics head on, and provide a forum for healthy discussion about future directions in both AI and neuroscience, and crucially, in research endeavours that aim to establish meaningful links between the two.

Workshop Description

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 cutting-edge artificial systems and the next generation of neuroscience experiments.

Call for papers

We invite contributions at the intersection between neuroscience and AI. In particular, we encourage work that identifies novel questions about the brain, which are informed by the recent successes of AI, and that establish key brain mechanisms that hold promise for further advancement of AI. The specific areas of interest are broad, including the study of recurrent dynamics, inductive biases to guide learning, global versus local learning rules, interpretability of network activity, connectivity structure, and more. Importantly, for a submission to be admissible, the topic must be at the intersection of AI and Neuroscience, i.e. they must leverage findings from one field to advance the other, or address common questions.

We highly encourage contributions that make their code or data publicly available. All accepted submissions will be invited to present a poster. A select number will be also invited to present short talks. Each accepted contribution must be presented by one of the authors in-person during the workshop (accommodations for extraneous circumstances can be arranged). Workshop contributions do not preclude future publication as proceedings will not be archived. However submissions will be made publicly available and open to community comments at the time of the workshop. Work that was previously published in an ML venue is not permitted.

Submissions must be within four pages (including references and appendices) and must be submitted in PDF format via the submission website. Reviews will be double-blind and the authors should ensure that submitted papers preserve their anonymity (i.e. anonymous author names, no reference to prior work that in any way identifies authors, etc.). Authors are encouraged but not required to use the NeurIPS style template (in anonymous mode). Evaluation will be according to the following criteria: relevance to the workshop theme, quality and originality of the work, clarity.

Important information about review process, public comments & access

In the spirit of transparency, during the review process submissions and reviews will be public, but anonymous. Submission authorship is made public upon decision. Final decisions will take into account review scores and diversity along several axes including topic, seniority, and gender. Authored comments by the public can be made throughout.

Invited speakers

Doina Precup

McGill/Mila/DeepMind

Tim Lillicrap

DeepMind/UCL

Organizers

Guillaume Lajoie

Université de Montréal / Mila

Eli Shlizerman

University of Washington

Maximilian Puelma Touzel

Université de Montréal / Mila

Jessica Thompson

Université de Montréal / Mila

Konrad Kording

University of Pennsylvania

Advisors

University of Washington

Université de Montréal / Mila

Sponsors