  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.
