Ming Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, USA.
Center for Neural Science, New York University, New York City, NY, USA.
Nat Neurosci. 2021 Jan;24(1):140-149. doi: 10.1038/s41593-020-00733-0. Epub 2020 Nov 9.
Neural activity exhibits complex dynamics related to various brain functions, internal states and behaviors. Understanding how neural dynamics explain specific measured behaviors requires dissociating behaviorally relevant and irrelevant dynamics, which is not achieved with current neural dynamic models as they are learned without considering behavior. We develop preferential subspace identification (PSID), which is an algorithm that models neural activity while dissociating and prioritizing its behaviorally relevant dynamics. Modeling data in two monkeys performing three-dimensional reach and grasp tasks, PSID revealed that the behaviorally relevant dynamics are significantly lower-dimensional than otherwise implied. Moreover, PSID discovered distinct rotational dynamics that were more predictive of behavior. Furthermore, PSID more accurately learned behaviorally relevant dynamics for each joint and recording channel. Finally, modeling data in two monkeys performing saccades demonstrated the generalization of PSID across behaviors, brain regions and neural signal types. PSID provides a general new tool to reveal behaviorally relevant neural dynamics that can otherwise go unnoticed.
神经活动表现出与各种大脑功能、内部状态和行为相关的复杂动力学。理解神经动力学如何解释特定的可测量行为需要分离行为相关和不相关的动力学，这是当前的神经动力学模型无法实现的，因为它们是在不考虑行为的情况下学习的。我们开发了优先子空间识别（PSID），这是一种在分离和优先考虑其行为相关动力学的同时对神经活动进行建模的算法。PSID 对两只猴子执行三维伸手和抓握任务的数据进行建模，结果表明，行为相关的动力学明显比其他情况下所暗示的更低维度。此外，PSID 发现了更能预测行为的独特旋转动力学。此外，PSID 更准确地学习了每个关节和记录通道的行为相关动力学。最后，对两只猴子执行扫视任务的数据进行建模表明，PSID 可以跨行为、大脑区域和神经信号类型进行推广。PSID 提供了一种新的通用工具，可以揭示否则可能被忽视的行为相关神经动力学。
