A stable, long - term cortical signature underlying consistent behavior

bioRxiv preprint first posted online Oct. 18, 2018.Animals readily execute learned motor behaviors in a consistent manner over long periods of time, yet similarly stable neural correlates remained elusive up to now. 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, are preserved across time. We recorded from populations of neurons in premotor, primary motor, and somatosensory cortices for up to two years as monkeys performed a reaching task. Intriguingly, despite steady turnover in the recorded neurons, the low-dimensional latent dynamics remained stable. Such stability allowed reliable decoding of behavioral features for the entire timespan, while fixed decoders based on the recorded neural activity degraded substantially. We posit that latent cortical dynamics within the manifold are the fundamental and stable building blocks underlying consistent behavioral execution.[ES]This work was supported in part by “Talent Attraction” Grant 2017-T2/TIC-5263 from the Community of Madrid (J.A.G.), by a grant from the Whitaker International Scholars Program (M.G.P.), and by Grant NS053603 from the National Institute of Neurological Disorder and Stroke (S.A.S. and L.E.M.).Peer reviewe

Local field potentials reflect cortical population dynamics in a region-specific and frequency-dependent manner

The spiking activity of populations of cortical neurons is well described by the dynamics of a small number of population-wide covariance patterns, the “latent dynamics”. These latent dynamics are largely driven by the same correlated synaptic currents across the circuit that determine the generation of local field potentials (LFP). Yet, the relationship between latent dynamics and LFPs remains largely unexplored. Here, we characterised this relationship for three different regions of primate sensorimotor cortex during reaching. The correlation between latent dynamics and LFPs was frequency-dependent and varied across regions. However, for any given region, this relationship remained stable throughout the behaviour: in each of primary motor and premotor cortices, the LFP-latent dynamics correlation profile was remarkably similar between movement planning and execution. These robust associations between LFPs and neural population latent dynamics help bridge the wealth of studies reporting neural correlates of behaviour using either type of recordings

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

Machine learning for neural decoding

Despite rapid advances in machine learning tools, the majority of neural decoding approaches still use traditional methods. Modern machine learning tools, which are versatile and easy to use, have the potential to significantly improve decoding performance. This tutorial describes how to effectively apply these algorithms for typical decoding problems. We provide descriptions, best practices, and code for applying common machine learning methods, including neural networks and gradient boosting. We also provide detailed comparisons of the performance of various methods at the task of decoding spiking activity in motor cortex, somatosensory cortex, and hippocampus. Modern methods, particularly neural networks and ensembles, significantly outperform traditional approaches, such as Wiener and Kalman filters. Improving the performance of neural decoding algorithms allows neuroscientists to better understand the information contained in a neural population and can help advance engineering applications such as brain machine interfaces