Long-term stability of cortical population dynamics underlying consistent behavior

Abstract

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. Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use:

Motor cortical dynamics are shaped by multiple distinct subspaces during naturalistic behavior

“…A naturalistic reach and grasp task to study sensorimotor integration. The surprisingly feedforward nature of motor cortical dynamics observed in prior studies may be attributed to examining behavioral tasks performed by highly trained subjects that were allowed to fully plan movements before executing them 10,14,15 . To fully understand the effect of sensory feedback on motor cortical dynamics, we designed a more naturalistic task that resulted with occasional behavioral errors.…”

“…Despite the large numbers of neurons in motor cortex, the activity of individual motor cortical neurons largely reflects some underlying, lower-dimensional, population-wide dynamics [7][8][9][10][11][12] . The dynamics are captured within the neural manifold 8 , which can be derived using dimensionality reduction techniques 13 .…”

Representational drift in the mouse visual cortex

“…We also demonstrated that the internal structure was stable over time across all visual areas, and that its stability depends on the number of cells included in the analysis. These results are consistent with several recent studies that showed that a stable structure (manifold) of population activity underlies a stable behavior (Rubin et al ., 2019; Bolding et al ., 2020; Gallego et al ., 2020; Pashkovski et al ., 2020). Our results are also consistent with a recent study in V1 that showed that high-dimensional population codes are more stable than low-dimensional population codes (Montijn et al ., 2016).…”

Population coupling predicts the plasticity of stimulus responses in cortical circuits

“…While analysing fluctuations in stimulus selectivity in network simulations allow us to perform a likefor-like comparison with what could be measured from the experimental data, there are alternative approaches which may capture more complex stimulus representations (Gallego et al, 2020), and the plasticity of these representations. Exploring how plasticity relates to neurons involvement in such low-dimensional latent dynamics would be an interesting direction for further work.…”