Machine learning methods enable researchers to discover statistical patterns in large datasets to solve a wide variety of tasks, including in neuroscience. Recent advances have led to an explosion in the scope and complexity of problems to which machine learning can be applied, with an accuracy rivaling or surpassing that of humans in some domains.
This virtual conference will illuminate the many ways machine learning and neuroscience intersect in the context of data analysis and modeling brain function, and how neuroscience can benefit from the machine learning revolution.
- Basic machine learning concepts and resources.
- Machine learning methods to automate analyses of large neuroscience datasets.
- Using deep network learning to gain insight into how the brain learns.
- Combining machine learning concepts with neuroscience theory to predict nervous system function and uncover general principles.
The conference will end with speakers sharing their views on promising future directions for both machine learning and neuroscience.
SfN members, including Institutional Program members, are able to register for the virtual conference at the special member rate of $50, saving $100.
Not an SfN member? Nonmembers can register for the virtual conference at the registration rate of $150. Join SfN or renew your membership to register for the virtual conference at the special member rate of $50 (saving $100) and receive access to other SfN member benefits.
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Click on each session title to reveal its description or download a one-page version of the agenda here.
Speakers: Floh Thiels, Sanjoy Dasgupta
Time: 11:00 a.m. – 11:45 a.m. EDT
The field of machine learning encompasses a broad range of data modeling and predictive tasks, and for each of these, a variety of approaches has been developed to address different application characteristics (e.g., different types of data). This session will provide a taxonomy of machine learning tasks and algorithms, illustrated by scientific applications.
Speakers: Kristin Branson, Sebastian Seung, Leila Wehbe, Nicholas Turner
Time: 12:00 p.m. – 1:00 p.m. EDT
Across neuroscience, from fMRI brain imaging of cognitive processing in humans to electron microscopy dissection of neuron connectivity in mice, scientists are producing datasets of unprecedented scale and complexity. Machine learning is a powerful tool for automating the processing and analysis of these datasets. In this session, Sebastian Seung and Leila Wehbe will discuss how machine learning is being used to analyze these types of data, and as a source of proposed models for the brain processes involved in the tasks behind the experiments. They will discuss different machine learning approaches and highlight pitfalls to avoid while pursuing them.
Speakers: Andrew Saxe, Kim Stachenfeld
Time: 1:30 p.m. – 2:30 p.m. EDT
In addition to providing state-of-the-art tools for neural data analysis, machine learning methods can be useful to neuroscience as models of neural systems themselves. This session will focus on how machine learning principles provide an orienting normative perspective for making sense of brain data. Andrew Saxe and Kim Stachenfeld will show how understanding properties of learning in an artificial context can translate to insights about learning in the biological context, and discuss how machine learning problems and unexplained neuroscience data can mutually inform each other. Their talks will be augmented with examples from their work highlighting ML-neuro translational insights.
Speakers: Floh Thiels, Scott Linderman, Srini Turaga
Time: 2:45 p.m. – 3:45 p.m. EDT
Unsupervised machine learning methods combine our prior hypotheses about the world with big data sets to discover new hypotheses. This session will describe the frontier of unsupervised machine learning algorithms and how they are being used to understand neuroscience data. Scott Linderman will present a short tutorial, “Finding Structure in Neural Data: From HMMs to Deep State Space Models,” which will cover both past and new ideas in state space modeling of neural data. Srini Turaga will present the use of variational autoencoders for Bayesian inference of spikes, synaptic inputs, and connectivity from calcium imaging and optogenetic perturbation experiments.
Speakers: Kristin Branson, Andrew Saxe, Kim Stachenfeld, Leila Wehbe
Time: 4:00 p.m. – 5:00 p.m. EDT
Panelists will share their views on the future of machine learning in neuroscience and answer questions from attendees.
On-Demand Data Blitz Sessions
This virtual conference will include short data blitz videos accessible to attendees. These videos feature neuroscientists describing their research applying computer vision to neuroscience problems. The videos are available on demand, so you can watch them throughout the live day or after the conference.
Data blitz videos will be provided by:
- Beth Cimini, The Broad Institute
- Eyrún Eyjólfsdóttir, Vicarious
- Larissa Heinrich, HHMI Janelia Research Campus
- Talmo Pereira, Princeton University