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Thu Dec 08 11:00 PM -- 09:30 AM (PST) @ Room 211
Brains and Bits: Neuroscience meets Machine Learning
Alyson Fletcher · Eva Dyer · Jascha Sohl-Dickstein · Joshua T Vogelstein · Konrad Koerding · Jakob H Macke
[ Video

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The goal of this workshop is to bring together researchers from neuroscience, deep learning, machine learning, computer science theory, and statistics for a rich discussion about how computer science and neuroscience can inform one another as these two fields rapidly move forward. We invite high quality submissions and discussion on topics including, but not limited to, the following fundamental questions: a) shared approaches for analyzing biological and artificial neural systems, b) how insights and challenges from neuroscience can inspire progress in machine learning, and c) methods for interpreting the revolutionary large scale datasets produced by new experimental neuroscience techniques.

Experimental methods for measuring neural activity and structure have undergone recent revolutionary advances, including in high-density recording arrays, population calcium imaging, and large-scale reconstructions of anatomical circuitry. These developments promise unprecedented insights into the collective dynamics of neural populations and thereby the underpinnings of brain-like computation. However, these next-generation methods for measuring the brain’s architecture and function produce high-dimensional, large scale, and complex datasets, raising challenges for analysis. What are the machine learning and analysis approaches that will be indispensable for analyzing these next-generation datasets? What are the computational bottlenecks and challenges that must be overcome?

In parallel to experimental progress in neuroscience, the rise of deep learning methods has shown that hard computational problems can be solved by machine learning algorithms that are inspired by biological neural networks, and built by cascading many nonlinear units. In contrast to the brain, artificial neural systems are fully observable, so that experimental data-collection constraints are not relevant. Nevertheless, it has proven challenging to develop a theoretical understanding of how neural networks solve tasks, and what features are critical to their performance. Thus, while deep networks differ from biological neural networks in many ways, they provide an interesting testing ground for evaluating strategies for understanding neural processing systems. Are there synergies between analysis methods for biological and artificial neural systems? Has the resurgence of deep learning resulted in new hypotheses or strategies for trying to understand biological neural networks? Conversely, can neuroscience provide inspiration for the next generation of machine-learning algorithms?

We welcome participants from a range of disciplines in statistics, applied physics, machine learning, and both theoretical and experimental neuroscience, with the goal of fostering interdisciplinary insights. We hope that active discussions among these groups can set in motion new collaborations and facilitate future breakthroughs on fundamental research problems.

Welcome and Opening Remarks (Talk)
Christos Papadimitriou : A computer scientist thinks about the brain (Keynote)
Cristina Savin : Spike-Based Probabilistic Computation (Talk)
Mitya Chklovskii : Toward Biologically Plausible Machine Learning: A Similarity Matching Approach (Talk)
Coffee Break 1a (plus posters) (Break)
Jonathan Pillow : Scalable Inference for Structured Hierarchical Receptive Field Models (Talk)
Emily Fox : Functional Connectivity in MEG via Graphical Models of Time Series (Talk)
Srini Turaga : Independence testing & Amortized inference, with neural networks, for neuroscience (Talk)
Lunch Day 1 (Lunch)
Il Memming Park : Dynamical Systems Interpretation of Neural Trajectories (Talk)
David Sussillo : LFADS - Latent Factor Analysis via Dynamical Systems (Talk)
Coffee Break 1b (plus posters) (Break)
Poster Session 1 (Poster Session)
Eva Dyer (Talk)
Michael Buice (Talk)
Stefan Mihalas : Modeling Optimal Context Integration in Cortical Columns (Talk)
Breakout Discussion Afternoon Session (Breakout Discussion)
Opening Remarks (Talk)
Yoshua Bengio : Toward Biologically Plausible Deep Learning (Keynote)
Surya Ganguli : Deep Neural Models of the Retinal Response to Natural Stimuli (Talk)
Max Welling : Making Deep Learning Efficient Through Sparsification (Talk)
Coffee Break 2a (plus posters) (Break)
David Cox : Predictive Coding for Unsupervised Feature Learning (Talk)
From Brains to Bits and Back Again (Discussion Panel)
Lunch Day 2 (Break)
Fred Hamprecht : Motif Discovery in Functional Brain Data (Talk)
Anima Anandkumar (Talk)
Coffee Break 2b (plus posters) (Break)
Poster Session 2 (Poster Session)
Kanitscheider : Training Recurrent Networks to Generate Hypotheses About How the Brain Solves Hard Navigation Problems (Talk)
Jorg Lucke : Probabilistic Inference and the Brain: Towards General, Scalable, and Deep Approximations (Talk)