Lars Buesing: May 22nd

Title: Dynamical System Models for Characterizing Multi-Electrode Recordings of Cortical Population Activity

Abstract: Multi-electrode techniques now make it possible to record from up to hundreds of cortical neurons simultaneously, and thus open the door to unprecedented insights into cortical neural population activity and the associated computations. However, in order to exploit this potential we need computationally tractable statistical methods that are able to see beyond signal and variability in individual neurons to structured activity that underlies reliable population computation. Such methods will very likely depend on analyzing the activity of the ensemble as a whole, rather than on simple single-neuron or pairwise analysis. In this talk I will argue that Dynamical System models, and more specifically Linear Dynamical Systems with Poisson observations (PLDS), meet these desiderata, while at the same time providing a parsimonious, statistically accurate description of the data. I will present a fast, robust algorithm for fitting PLDS models, which is based on spectral subspace methods. This algorithm substantially improves over standard approximate Expectation-Maximization for PLDS models in terms of both computational efficiency as well as quality of estimated parameters, hence greatly facilitating the application of these models to real multi-electrode recordings. Finally, I will show how Dynamical System models can be used to characterize fundamental dynamical properties of multi-electrode recordings from motor areas of awake, behaving macaque monkeys.This analysis reveals that different epochs of task-relevant behavior manifest themselves in different dynamics of the recorded neural population.