Long-term stability of cortical population dynamics underlying consistent behavior

Animals readily execute learned behaviors in a consistent manner over long periods of time, yet no equally stable neural correlate has been demonstrated. How does the cortex achieve this stable control? Using the sensorimotor system as a model of cortical processing, we investigated the hypothesis that the dynamics of neural latent activity, which capture the dominant co-variation patterns within the neural population, must be preserved across time. We recorded from populations of neurons in premotor, primary motor, and somatosensory cortices as monkeys performed a reaching task, for up to two years. Intriguingly, despite steady turnover in the recorded neurons, the low-dimensional latent dynamics remained stable. The stability allowed reliable decoding of behavioral features for the entire timespan, while fixed decoders based directly on the recorded neural activity degraded substantially. We posit that stable latent cortical dynamics within the manifold are the fundamental building blocks underlying consistent behavioral execution.This work was supported in part by Grant FP7-PEOPLE-2013-IOF-627384 from the Commission of the European Union and Grant 2017-T2/TIC-5263 from the Community of Madrid (J.A.G.), by Grant F31-NS092356 from the National Institute of Neurological Disorder and Stroke and Grant T32-HD07418 from the National Institute of Child Health and Human Development (M.G.P.), by Grant DGE-1324585 from the National Science Foundation (R.H.C.), and by Grants NS095251 (L.E.M.) and NS053603 (S.A.S. and L.E.M.) from the National Institute of Neurological Disorder and Stroke.Peer reviewe