INVITED: Computational Methods of Biological Exploration

Abstract

Our technical ability to collect data about biological systems far outpaces our ability to understand them. Historically, for example, we have had complete and explicit genomes for almost two decades, but we still have no idea what many genes do. More recently a similar situation has arisen, where we can reconstruct huge neural circuits, and/or watch them operate in the brain, but still don’t know how they work. This talk covers this second and newer problem, understanding neural circuits. We introduce a variety of computational tools currently being used to attack this data-rich, understanding-poor problems. Examples include dimensionality reduction for nonlinear systems, looking for known and proposed circuits, and using machine learning for parameter estimation. One general theme is the use of biological priors, to help fill in unknowns, see if proposed solutions are feasible, and more generally aid understanding.