Speaker
Description
Animals, including humans, perform learned actions with remarkable consistency for years after acquiring a skill. What is the neural correlate of this stability? We explore this question from the perspective of neural populations. Recent work suggests that the building blocks of neural function may be the activation of population-wide activity patterns, the neural modes, rather than the independent modulation of individual neurons. These neural modes, the dominant co-variation patterns of population activity, define a low dimensional neural manifold that captures most of the variance in the recorded neural activity. We refer to the time-dependent activation of the neural modes as their latent dynamics. We hypothesize that the ability to perform a given behavior in a consistent manner requires that the latent dynamics underlying the behavior also be stable.
A dynamic alignment method allows us to examine the long term stability of the latent dynamics despite unavoidable changes in the set of neurons recorded via chronically implanted microelectrode arrays. We use the sensorimotor system as a model of cortical processing, and find remarkably stable latent dynamics for up to two years across three distinct cortical regions, despite ongoing turnover of the recorded neurons. The stable latent dynamics, once identified, allows for the prediction of various behavioral features via mapping models whose parameters remain fixed throughout these long timespans. We conclude that latent cortical dynamics within the task manifold are the fundamental and stable building blocks underlying consistent behavior
