Dimensionality Reduction and Population Dynamics in Neural Data

The brain represents and processes information through the activity of many neurons whose firing patterns are correlated with each other in non-trivial ways. These correlations, in general, imply that the activity of a population of neurons involved in a task has a lower dimensional representation. Naturally, then, discovering and understanding such representations are important steps in understanding the operations of the nervous system, and theoretical and experimental neuroscientists have been making interesting progress on this subject. The aim of this conference is to gather together a number of key players in the effort for developing methods for dimensionality reduction in neural data and studying the population dynamics of networks of neurons from this angle. We aim to review the current approaches to the problem, identify the major questions that need to be addressed in the future, and discuss how we should move forward with those questions.