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 Workshop “proceedings” will consist of links to unpublished arXiv or bioarXiv papers that are of exceptional quality and are well aligned with the workshop scope.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.
|08:40 AM||Opening Remarks (Talk)|
|Eva Dyer, Will Gray Roncal|
|09:00 AM||Can brain data be used to reverse engineer the algorithms of human perception? (Talk)|
|James J DiCarlo|
|09:35 AM||Backpropagation and deep learning in the brain (Talk)|
|09:55 AM||Algorithms, tools, and progress in connectomic reconstruction of neural circuits (Discussion)|
|10:15 AM||Multimodal deep learning for natural human neural recordings and video (Talk)|
|10:35 AM||Coffee Break (Break)|
|11:00 AM||More Steps towards Biologically Plausible Backprop (Talk)|
|11:40 AM||Panel on "What neural systems can teach us about building better machine learning systems" (Discussion)|
|Timothy Lillicrap, James J DiCarlo, Christopher Rozell, Viren Jain, Nathan Kutz, Will Gray Roncal, Bing Brunton|
|12:20 PM||Lunch Break (Break)|
|01:40 PM||Discovery of governing equations and biological principles from spatio-temporal time-series recordings (Talk)|
|02:15 PM||Large scale calcium imaging data analysis for the 99% (Talk)|
|02:35 PM||Machine learning for cognitive mapping (Talk)|
|02:55 PM||Dealing with clinical heterogeneity in the discovery of new biomarkers of brain disorders (Talk)|
|03:15 PM||Morphological error detection for connectomics (Talk)|
|03:25 PM||Poster Spotlights (Talk)|
|03:45 PM||Poster Session (Posters)|
|04:30 PM||Deep nets meet real neurons: pattern selectivity of V4 through transfer learning and stability analysis (Talk)|
|05:05 PM||Mapping brain structure and function with deep learning (Talk)|
|05:25 PM||bossDB: A Petascale Database for Large-Scale Neuroscience Informing Machine Learning (Talk)|
|05:45 PM||Machine vision and learning for extracting a mechanistic understanding of neural computation (Talk)|
|06:05 PM||Closing Panel: Analyzing brain data from nano to macroscale (Discussion)|
|Will Gray Roncal, Eva Dyer|