习得运动-发声行为期间鸣禽 RA 与 HVC 中的神经群体动力学
Neural population dynamics in songbird RA and HVC during learned motor-vocal behavior
AI总结：
本研究利用 Neuropixels 记录鸣禽 HVC 与 RA，揭示二者发声相关群体活动均处于低维流形，但 RA 与发声输出连续对应更稳定，潜在变量还支持跨新子群体泛化解码。
摘要：
复杂的习得运动行为需要跨多个脑区的大规模神经活动协调，但我们对与同一行为相关的不同脑区内群体层面动力学的理解仍然有限。在此，我们研究清醒鸣唱鸣禽习得发声产生背后的神经群体动力学。我们使用 Neuropixels 探针，同时记录发声运动通路中两个脑区内神经元群体的细胞外活动。与非人灵长类动物在基于肢体的运动任务期间的观察一致，我们表明前运动核团 HVC 和运动核团 RA 中的群体层面活动都组织在低维神经流形上；在鸣唱行为期间，协调的神经活动可由具有时间结构的轨迹很好地描述。HVC 和 RA 的潜在轨迹都提供了预测鸣唱音节之间发声序列转换的相关信息。然而，这些潜在轨迹的动力学在两个脑区之间存在差异。我们的状态空间模型表明，RA 潜在空间与发声输出之间存在独特且随时间连续的对应关系，而 HVC 的相应关系表现出更高程度的神经变异性。随后，我们证明，从 HVC 和 RA 的神经潜在变量及脉冲活动中，都可以实现对连续发声输出的相当高保真重建。然而，与使用脉冲活动的解码模型不同，使用神经潜在变量的解码模型可泛化到每个脑区中的新子群体，这与限制 HVC 和 RA 中发声运动活动的、保持不变的流形的存在相一致。
英文摘要：
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.
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