Toward the explainability, transparency, and universality of machine learning for behavioral classification in neuroscience.
Toward the explainability, transparency, and universality of machine learning for behavioral classification in neuroscience.
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DOI：
10.1016/j.conb.2022.102544
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发表时间：
2022-04
影响因子：
5.7
通讯作者：
中科院分区：
文献类型：
--
作者：
通过机器学习技术使用严格的行为学观察来理解大脑功能（计算神经行为学）是一种快速发展的方法，有望显著改变行为神经科学的通常执行方式。随着用于自动跟踪和行为识别的开源平台的发展，这些方法现在可以被广泛的神经科学家所使用，尽管预算和计算经验各不相同。重要的是，这种采用通过消除手动偏见和识别以前未知的行为库，使该领域朝着对行为和大脑功能的共同理解方向发展。虽然不太明显，但这一运动的另一个后果是引入了分析工具，这些工具增加了基于机器的行为分类的可解释性，透明度和普遍性，无论是在研究小组内部还是在研究小组之间。在这里，我们专注于三个主要的应用程序，这些机器模型的解释性工具和指标的驱动器朝着行为（i）标准化，（ii）专业化，和（iii）可解释性。我们提供了有关计算神经行为学中可解释性工具使用的观点，并详细说明了为什么这是该领域扩展的必要下一步。具体来说，作为行为神经科学中的一种可能的解决方案，我们建议通过Shapley Additive Decomposition（SHAP）使用Shapley值作为人类注释的可解释性以及监督和无监督行为机器学习分析的资源。
The use of rigorous ethological observation via machine learning techniques to understand brain function (computational neuroethology) is a rapidly growing approach that is poised to significantly change how behavioral neuroscience is commonly performed. With the development of open-source platforms for automated tracking and behavioral recognition, these approaches are now accessible to a wide array of neuroscientists despite variations in budget and computational experience. Importantly, this adoption has moved the field towards a common understanding of behavior and brain function through the removal of manual bias and the identification of previously unknown behavioral repertoires. Although less apparent, another consequence of this movement is the introduction of analytical tools that increase the explainabilty, transparency, and universality of the machine-based behavioral classifications both within and between research groups. Here, we focus on three main applications of such machine model explainabilty tools and metrics in the drive towards behavioral (i) standardization, (ii) specialization, and (iii) explainability. We provide a perspective on the use of explainability tools in computational neuroethology, and detail why this is a necessary next step in the expansion of the field. Specifically, as a possible solution in behavioral neuroscience, we propose the use of Shapley values via Shapley Additive Explanations (SHAP) as a resource for the explainability of human annotation, as well as supervised and unsupervised behavioral machine learning analysis.
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影响因子：
7.7
作者：
Gruene, Tina M.;Flick, Katelyn;Shansky, Rebecca M.
通讯作者：
Shansky, Rebecca M.
影响因子：
3
作者：
Greenberg GD;Laman-Maharg A;Campi KL;Voigt H;Orr VN;Schaal L;Trainor BC
通讯作者：
Trainor BC
影响因子：
7.7
作者：
Geuther BQ;Peer A;He H;Sabnis G;Philip VM;Kumar V
通讯作者：
Kumar V
影响因子：
48
作者：
Dunn TW;Marshall JD;Severson KS;Aldarondo DE;Hildebrand DGC;Chettih SN;Wang WL;Gellis AJ;Carlson DE;Aronov D;Freiwald WA;Wang F;Ölveczky BP
通讯作者：
Ölveczky BP
影响因子：
14.9
作者：
Bogue, Molly A.;Philip, Vivek M.;Chesler, Elissa J.
通讯作者：
Chesler, Elissa J.