Advances in optics, chemistry, and physics have revolutionized the development of experimental methods for measuring neural activity and structure. Some of the next generation methods for neural recording, promise extremely large and detailed measurements of the brain’s architecture and function. The goal of this workshop is to provide an open forum for the discussion of a number of important questions related to how machine learning can aid in the analysis of these next generation neural datasets. What are some of the new machine learning and analysis problems that will arise as new experimental methods come online? What are the right distributed and/or parallel processing computational models to use for these different datasets? What are the computational bottlenecks/challenges in analyzing these next generation datasets?
In the morning, the goal will be to discuss new experimental techniques and the computational issues associated with analyzing the datasets generated by these techniques. The morning portion of the workshop will be organized into three hour-long sessions. Each session will start with a 30 minute overview of an experimental method, presented by a leading experimentalist in this area. Afterwards, we will have a 20 minute follow up from a computational scientist that will highlight the computational challenges associated with the technique.
In the afternoon, the goal will be to delve deeper into the kinds of techniques that will be needed to make sense of the data described in the morning. To highlight two computational approaches that we believe hold promise, we will have two 50 minute long methods talks. These talks will be followed by a scientist with big-data experience outside of neuroscience with the goal of thinking about organization, objectives, and pitfalls. Lastly we will have plenty of time for free form discussion and hold a poster session (open call for poster submissions). We envision that this workshop will provide a forum for computational neuroscientists and data scientists to discuss the major challenges that we will face in analyzing big neural datasets over the next decade.