BigNeuro 2017: Analyzing brain data from nano to macroscale

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 Workshop “proceedings” will consist of links to unpublished arXiv or bioarXiv papers that are of exceptional quality and are well aligned with the workshop scope.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.