Quantifying behavior to understand the brain.
Quantifying behavior to understand the brain.
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DOI：
10.1038/s41593-020-00734-z
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发表时间：
2020-12
影响因子：
25
通讯作者：
Murthy M
中科院分区：
文献类型：
--
作者：
Pereira TD;Shaevitz JW;Murthy M
在过去的几年里，出现了许多方法来以以前无法想象的分辨率自动量化动物行为。这开辟了计算行为学的新领域，并且在不久的将来，将有可能近乎完整地量化动物在其环境中导航时所做的事情。人们逐渐认识到理解行为是追求神经科学问题的必要（甚至先决）步骤，这反映了改进我们表征行为的技术的重要性。然而，这些方法的使用并不限于研究野外或严格的行为学环境中的行为。现代行为量化工具可以应用于历史上用于将大脑与行为联系起来的所有方法，从心理物理学到认知任务，通过对动物如何完成这些任务的丰富描述来增强这些测量。在这里，我们回顾了量化行为方面的最新技术进展，特别是在跟踪动物运动和表征这些动态结构的方法方面。我们讨论了行为量化仍然面临的开放挑战，并强调了有希望的未来方向，重点关注深度学习的新兴方法，深度学习是推动该领域快速发展的核心技术。然后我们讨论如何利用行为的定量描述将大脑活动与动物运动联系起来，最终目标是解决神经回路、认知过程和行为之间的关系。
Over the past years, numerous methods have emerged to automate the quantification of animal behavior at a resolution not previously imaginable. This has opened up a new field of computational ethology and will, in the near future, make it possible to quantify in near completeness what an animal is doing as it navigates its environment. The importance of improving the techniques with which we characterize behavior is reflected in the emerging recognition that understanding behavior is an essential (or even prerequisite) step to pursuing neuroscience questions. The use of these methods, however, is not limited to studying behavior in the wild or in strictly ethological settings. Modern tools for behavioral quantification can be applied to the full gamut of approaches that have historically been used to link brain to behavior, from psychophysics to cognitive tasks, augmenting those measurements with rich descriptions of how animals navigate those tasks. Here we review recent technical advances in quantifying behavior, particularly in methods for tracking animal motion and characterizing the structure of those dynamics. We discuss open challenges that remain for behavioral quantification and highlight promising future directions, with a strong emphasis on emerging approaches in deep learning, the core technology that has enabled the markedly rapid pace of progress of this field. We then discuss how quantitative descriptions of behavior can be leveraged to connect brain activity with animal movements, with the ultimate goal of resolving the relationship between neural circuits, cognitive processes and behavior.
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影响因子：
7.7
作者：
Cande J;Namiki S;Qiu J;Korff W;Card GM;Shaevitz JW;Stern DL;Berman GJ
通讯作者：
Berman GJ
DOI：
10.1073/pnas.1607601113
发表时间：
2016-10-18
影响因子：
11.1
作者：
Berman, Gordon J.;Bialek, William;Shaevitz, Joshua W.
通讯作者：
Shaevitz, Joshua W.
影响因子：
5.4
作者：
Berman GJ
通讯作者：
Berman GJ
影响因子：
16.6
作者：
Bala, Praneet C.;Eisenreich, Benjamin R.;Zimmermann, Jan
通讯作者：
Zimmermann, Jan
影响因子：
7.6
作者：
Coffey, Kevin R.;Marx, Russell G.;Neumaier, John F.
通讯作者：
Neumaier, John F.