美国南加州大学维特比工程学院Maryam M. Shanechi通过优先子空间识别（PSID）对行为相关的神经动力学建模。相关论文发表在2020年11月9日出版的《自然-神经科学》杂志上。
他们开发了PSID，这是一种对神经活动建模的算法，同时可以分离和确定其行为相关的动力学优先级。通过对两只猴子的数据建模，它们完成了三维到达和抓握任务，PSID显示与行为相关的动力学维数明显低于其他隐含维数。此外，PSID发现了不同的旋转动力学，可以更好地预测行为。
此外，PSID更准确地了解了每个关节和记录通道的行为相关动力学。最后，在两只进行扫视的猴子中对数据进行建模，证明了PSID在行为、大脑区域和神经信号类型之间的普遍性。PSID提供了一个通用的新工具来揭示行为相关的神经动力学，否则这些神经动力学可能不会被注意到。
据悉，神经活动表现出与各种大脑功能、内部状态和行为有关的复杂动力学。理解神经动力学如何解释特定的测量行为需要分离行为相关和不相关的动力学，由于在不考虑行为的情况下学习它们，这是当前神经动力学模型无法实现的。
附：英文原文
Title: Modeling behaviorally relevant neural dynamics enabled by preferential subspace identification
Author: Omid G. Sani, Hamidreza Abbaspourazad, Yan T. Wong, Bijan Pesaran, Maryam M. Shanechi
Issue&Volume: 2020-11-09
Abstract: 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. This work develops PSID, a dynamic modeling method to dissociate and prioritize neural dynamics relevant to a given behavior.
DOI: 10.1038/s41593-020-00733-0
Source: https://www.nature.com/articles/s41593-020-00733-0
Nature Neuroscience：《自然—神经科学》，创刊于1998年。隶属于施普林格·自然出版集团，最新IF：28.771
官方网址：https://www.nature.com/neuro/
投稿链接：https://mts-nn.nature.com/cgi-bin/main.plex
本期文章：《自然—神经科学》：Online/在线发表
