In the past few years, the field of molecular biology of the brain has been transformed from hypothesis-based experiments to high-throughput experiments. The massive growth of data, including measures of the brain transcriptome, methylome and proteome, now raises new questions in neurobiology and new challenges in analysis of these complex and vast datasets. While many of these challenges are shared with other computational biology studies, the complexity of the brain poses special challenges. Brain genomics data includes high-resolution molecular imagery, developmental time courses and most importantly, underlies complex behavioral phenotypes and psychiatric diseases. New methods are needed to address questions about the brain-wide, genome-wide and life-long genomic patterns in the brain and their relation to brain functions like plasticity and information processing.

The goal of the workshop is to bring together people from the neuroscience, cognitive science and the machine learning community. It aims to ease the path for scientists to connect the wealth of genomic data to the issues of cognition and learning that are central to NIPS, with an eye to the emerging high-throughput behavioral data which many are gathering. We invite contributed talks on novel methods of analysis to brain genomics, as well as techniques to make meaningful statistical relationships to phenotypes.

The target audience includes two main groups: people interested in developing machine learning approaches to neuroscience, and people from neuroscience and cognitive science interested in connecting their work to brain genomics.