NeuroPB：利用预训练行为表示扩展神经解码
NeuroPB: Scaling Neural Decoding with Pretrained Behavioral Representations
AI总结：
本文提出NeuroPB框架，通过预训练大规模行为表示并对齐有限配对神经-行为数据，提升神经解码性能，在猕猴数据集上R²提升11%与8%，且泛化能力强，为高效BCI提供可行方向。
AI中文摘要：
从神经活动中解码连续运动轨迹是开发实用脑机接口（BCI）的核心，但当前神经解码器受限于神经记录的规模有限与异质性问题。相比之下，行为数据可从人类、动物、模拟系统及机器人系统中更便捷地采集，且规模大得多。本文提出NeuroPB框架，通过迁移预训练行为表示的知识来扩展神经解码：首先在大规模运动行为数据上预训练运动编码器，再利用有限的配对神经-行为记录将神经活动与生成的行为表示空间对齐，随后优化神经编码器与轻量运动解码器，以从对齐的神经表示中重构连续运动。在多个猕猴运动数据集上，行为预训练可提升轨迹解码性能：与从头训练运动编码器相比，中心向外任务的R²提升11%，随机目标任务提升8%。值得注意的是，在机器人轨迹上预训练的性能可与在猕猴轨迹上预训练的性能相当，表明生物与人工模型间存在可迁移的运动学结构。此外，当神经数据量固定时，解码性能随机器人预训练数据的规模与多样性增加而提升。预训练还增强了跨记录会话、跨受试者及跨运动任务的泛化能力，仅需10%的校准数据即可达到从头训练的性能。总体而言，这些结果确立了行为预训练是神经解码的可扩展来源，为神经数据有限情况下开发高性能、校准高效的BCI提供了可行路径。
英文摘要：
Decoding continuous motor trajectories from neural activity is essential for developing practical brain-computer interfaces (BCIs). However, current neural decoders are constrained by the limited scale and heterogeneity of neural recordings. In contrast, behavioral data can be collected more readily and at substantially larger scale from humans, animals, simulations, and robotic systems. Here, we introduce NeuroPB, a framework that scales neural decoding by transferring knowledge from pretrained behavioral representations. NeuroPB first pretrains a motor encoder on large-scale motor behavior data and then aligns neural activity with the resulting behavioral representation space using a limited set of paired neural-behavioral recordings. A neural encoder and lightweight motor decoder are subsequently optimized to reconstruct continuous movement from the aligned neural representations. Across multiple macaque motor datasets, behavioral pretraining improves trajectory decoding, including an 11% $R^2$ increase on center-out and 8% on random-target compared with training the motor encoder from scratch. Notably, pretraining on robotic trajectories achieves performance comparable to pretraining on macaque trajectories, demonstrating that transferable kinematic structure is shared across biological and artificial models. Moreover, decoding performance improves as the scale and diversity of robotic pretraining data increase, when the amount of neural data is fixed. Pretraining also enhances generalization across recording sessions, subjects, and motor tasks, with only 10% calibration needed to match training from scratch. Overall, these results establish behavioral pretraining as a scalable source for neural decoding and provide a promising route toward high-performance and calibration-efficient BCIs under limited neural data.
