Long-term stability of cortical population dynamics underlying consistent behavior

Juan A. Gallego*, Matthew G. Perich, Raeed H. Chowdhury, Sara A. Solla, Lee E. Miller

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

46 Scopus citations

Abstract

Animals readily execute learned behaviors in a consistent manner over long periods of time, and 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 captures 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 2 years. Intriguingly, despite a 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.

Original languageEnglish (US)
Pages (from-to)260-270
Number of pages11
JournalNature neuroscience
Volume23
Issue number2
DOIs
StatePublished - Feb 1 2020

ASJC Scopus subject areas

  • Neuroscience(all)

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