Neural population dynamics in songbird RA and HVC during learned motor-vocal behavior

TL;DR

This study investigates neural population dynamics in songbird brain regions HVC and RA during learned vocal behavior, revealing low-dimensional neural manifolds and distinct trajectory dynamics that predict vocal sequences.

Contribution

It provides new insights into the population-level neural dynamics in HVC and RA during singing, highlighting differences in their trajectory structures and their relation to vocal output.

Findings

Neural activity in HVC and RA is organized on low-dimensional manifolds.

Latent trajectories in HVC and RA predict vocal sequence transitions.

Decoding models from neural latents generalize across sub-populations.

Abstract

Complex, learned motor behaviors involve the coordination of large-scale neural activity across multiple brain regions, but our understanding of the population-level dynamics within different regions tied to the same behavior remains limited. Here, we investigate the neural population dynamics underlying learned vocal production in awake-singing songbirds. We use Neuropixels probes to record the simultaneous extracellular activity of populations of neurons in two regions of the vocal motor pathway. In line with observations made in non-human primates during limb-based motor tasks, we show that the population-level activity in both the premotor nucleus HVC and the motor nucleus RA is organized on low-dimensional neural manifolds upon which coordinated neural activity is well described by temporally structured trajectories during singing behavior. Both the HVC and RA latent trajectories provide relevant information to predict vocal sequence transitions between song syllables. However, the dynamics of these latent trajectories differ between regions. Our state-space models suggest a unique and continuous-over-time correspondence between the latent space of RA and vocal output, whereas the corresponding relationship for HVC exhibits a higher degree of neural variability. We then demonstrate that comparable high-fidelity reconstruction of continuous vocal outputs can be achieved from HVC and RA neural latents and spiking activity. Unlike those that use spiking activity, however, decoding models using neural latents generalize to novel sub-populations in each region, consistent with the existence of preserved manifolds that confine vocal-motor activity in HVC and RA.