一种用于检测替代性试错行为的机器学习方法,bioRxiv - Neuroscience

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一种用于检测替代性试错行为的机器学习方法
bioRxiv - Neuroscience Pub Date : 2021-03-04 , DOI: 10.1101/2021.03.03.433785
Jesse T. Miles , Kevan S. Kidder , Ziheng Wang , Yiru Zhu , David H. Gire , Sheri J. Y. Mizumori

替代性试错行为（VTE）表示决策过程中犹豫不决的时期，并已被提出作为审议的行为标志。为了了解行为和神经动力学之间这些假定桥梁的神经基础，研究人员需要能够轻松地区分VTE和非VTE的能力。在这里，我们利用一整套基于轨迹的特征和标准机器学习分类器，从非VTE中识别出在高架迷宫上执行空间延迟交替任务（SDA）的大鼠中的VTE。我们还表明，先前报道的海马场电势振荡特征可用于相同类型的分类器，以将VTE与非VTE分开，并具有较高的机会表现。然而，我们告诫使用海马种群动态进行分类器的成功并不足以识别发生VTE的试验，并且表明与仅基于轨迹的特征相比，将基于振动的特征与基于轨迹的特征相结合会降低分类器的性能。总体而言，我们提出了一套适用于基于轨迹的VTE分类的标准功能，并支持先前的建议，即VTE受网络支持，包括但不限于海马体。

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A machine learning approach for detecting vicarious trial and error behaviors

Vicarious trial and error behaviors (VTEs) indicate periods of indecision during decision-making, and have been proposed as a behavioral marker of deliberation. In order to understand the neural underpinnings of these putative bridges between behavior and neural dynamics, researchers need the ability to readily distinguish VTEs from non-VTEs. Here we utilize a small set of trajectory-based features and standard machine learning classifiers to identify VTEs from non-VTEs for rats performing a spatial delayed alternation task (SDA) on an elevated plus maze. We also show that previously reported features of the hippocampal field potential oscillation can be used in the same types of classifiers to separate VTEs from non-VTEs with above chance performance. However, we caution that the modest classifier success using hippocampal population dynamics is not sufficient for identifying trials where VTEs occur, and show that combining oscillation-based features with trajectory-based features degrades classifier performance compared to trajectory-based features alone. Overall, we propose a standard set of features useful for trajectory-based VTE classification and support previous suggestions that VTEs are supported by a network including, but likely extending beyond, the hippocampus.

更新日期：2021-03-05

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