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Datasets in neuroscience are increasing in size at alarming rates relative to our ability to analyze them. This workshop aims at discussing new frameworks for processing and making sense of large neural datasets.
The morning session will focus on approaches for processing large neuroscience datasets. Examples include: distributed + high-performance computing, GPU and other hardware accelerations, spatial databases and other compression schemes used for large neuroimaging datasets, online machine learning approaches for handling large data sizes, randomization and stochastic optimization.
The afternoon session will focus on abstractions for modelling large neuroscience datasets. Examples include graphs, graphical models, manifolds, mixture models, latent variable models, spatial models, and factor learning.
In addition to talks and discussions, we plan to have papers submitted and peer reviewed. Workshop “proceedings” will consist of links to unpublished arXiv or bioarXiv papers that are of exceptional quality and are well aligned with the workshop scope. Some accepted papers will also be invited for an oral presentation; the remaining authors will be invited to present a poster.
Sat 8:40 a.m. - 9:00 a.m.
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Opening Remarks
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Talk
)
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Eva Dyer · William Gray Roncal 🔗 |
Sat 9:00 a.m. - 9:35 a.m.
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Can brain data be used to reverse engineer the algorithms of human perception?
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Talk
)
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James J DiCarlo 🔗 |
Sat 9:35 a.m. - 9:55 a.m.
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Backpropagation and deep learning in the brain
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Talk
)
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Timothy Lillicrap 🔗 |
Sat 9:55 a.m. - 10:15 a.m.
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Algorithms, tools, and progress in connectomic reconstruction of neural circuits
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Discussion
)
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Viren Jain 🔗 |
Sat 10:15 a.m. - 10:35 a.m.
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Multimodal deep learning for natural human neural recordings and video
(
Talk
)
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Bingni Brunton 🔗 |
Sat 10:35 a.m. - 11:00 a.m.
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Coffee Break
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🔗 |
Sat 11:00 a.m. - 11:35 a.m.
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More Steps towards Biologically Plausible Backprop
(
Talk
)
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Yoshua Bengio 🔗 |
Sat 11:40 a.m. - 12:20 p.m.
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Panel on "What neural systems can teach us about building better machine learning systems"
(
Discussion
)
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Timothy Lillicrap · James J DiCarlo · Christopher Rozell · Viren Jain · Nathan Kutz · William Gray Roncal · Bingni Brunton 🔗 |
Sat 12:20 p.m. - 1:40 p.m.
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Lunch Break
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🔗 |
Sat 1:40 p.m. - 2:15 p.m.
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Discovery of governing equations and biological principles from spatio-temporal time-series recordings
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Talk
)
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Nathan Kutz 🔗 |
Sat 2:15 p.m. - 2:35 p.m.
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Large scale calcium imaging data analysis for the 99%
(
Talk
)
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Eftychios Pnevmatikakis 🔗 |
Sat 2:35 p.m. - 2:55 p.m.
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Machine learning for cognitive mapping
(
Talk
)
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Gaël Varoquaux 🔗 |
Sat 2:55 p.m. - 3:15 p.m.
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Dealing with clinical heterogeneity in the discovery of new biomarkers of brain disorders
(
Talk
)
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Christian Dansereau 🔗 |
Sat 3:15 p.m. - 3:25 p.m.
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Morphological error detection for connectomics
(
Talk
)
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David Rolnick 🔗 |
Sat 3:25 p.m. - 3:45 p.m.
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Poster Spotlights
(
Talk
)
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🔗 |
Sat 3:45 p.m. - 4:30 p.m.
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Poster Session
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Posters
)
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🔗 |
Sat 4:30 p.m. - 5:05 p.m.
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Deep nets meet real neurons: pattern selectivity of V4 through transfer learning and stability analysis
(
Talk
)
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Bin Yu 🔗 |
Sat 5:05 p.m. - 5:25 p.m.
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Mapping brain structure and function with deep learning
(
Talk
)
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Vince Calhoun 🔗 |
Sat 5:25 p.m. - 5:45 p.m.
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bossDB: A Petascale Database for Large-Scale Neuroscience Informing Machine Learning
(
Talk
)
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Nathan Drenkow 🔗 |
Sat 5:45 p.m. - 6:05 p.m.
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Machine vision and learning for extracting a mechanistic understanding of neural computation
(
Talk
)
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Kristin Branson 🔗 |
Sat 6:05 p.m. - 6:30 p.m.
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Closing Panel: Analyzing brain data from nano to macroscale
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Discussion
)
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William Gray Roncal · Eva Dyer 🔗 |
Author Information
Eva Dyer (Georgia Institute of Technology)
Eva Dyer is an Assistant Professor in the Wallace H. Coulter Department of Biomedical Engineering at the Georgia Institute of Technology and Emory University. Dr. Dyer completed her M.S. and Ph.D degrees in Electrical & Computer Engineering at Rice University in 2011 and 2014, respectively. Eva's research interests lie at the intersection of data science, machine learning, and neuroscience.
Gregory Kiar (McGill University)
Greg Kiar is a PhD Student in the Department of Biomedical Engineering in the Montreal Neurological Institute with Alan C. Evans at McGill University. Mr. Kiar completed his B.En in Biomedical and Electrical Engineering from Carleton University in 2014, and his M.S.E in Biomedical Engineering from Johns Hopkins University with Joshua T. Vogelstein in 2016, in which time he developed and optimized a pipeline for structural connectome estimation at the macroscale in humans. Greg is a Healthy Brain for Healthy Lives PhD fellow, and his work continues to primarily focus on increasing the accessibility, robustness, and reliability of computational neuroscience pipelines and workflows.
William Gray Roncal (Johns Hopkins University)
Konrad P Koerding (Northwestern)
Joshua T Vogelstein (The Johns Hopkins University)
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