Reprinted from Neuron, 88 /16, Alexander B. Wiltschko, Matthew J. Johnson, Giuliano Iurilli, Ralph E. Peterson, Jesse M. Katon, Stan L. Pashkovski, Victoria E. Abraira, Ryan P. Adams, Sandeep Robert Datta, Mapping Sub-Second Structure in Mouse Behavior, 1121 - 35, 2015, with permission from Elsevier

行为的秘密语言

Speaking in San Diego at last November’sSociety for Neuroscience meeting，，，， Sandeep Robert (Bob) Datta described how his computational analysis of the behavior of mice, currently the onlypublished studyof this kind on mammals, reveals a modular structure. He emphasized the unsupervised nature of the machine learning he trained on the 3-D pose dynamics of  freely moving animals.   A subsecond structured pattern fell out of the data themselves – initially derived from videos of mice placed individually in buckets – but the same temporal patterning was detected in other environments as well.

数据的数学建模确定了大约60个音节的曲目，每个音节在姿势空间中平均持续300毫秒，并通过动态过渡分开。改变环境（例如将Fox气味放在竞技场的一个地方）引起了厌恶行为，包括相同的模块，但具有不同的频率和过渡概率。

The study, described in a video (below) from the Datta lab, has now been extended in unpublished work to demonstrate how dissecting behaviour by this approach provides a sensitive read-out of the effects of drugs and disease mutations.

In an experiment in which each of 500 mice was given one of 15 commonly used psychoactive drugs, Datta’s analysis using his model finds that each leaves a different behavioral fingerprint, diagnostic of the particular drug an individual mouse had been given.

他的behavioral phenotyping of two genetic mouse models for autism – CNTNAP2/- and 16p11.2 –recentlyreported as hyperactive after a battery of traditional behavioral assays，，，，reveals clear differences in the alterations of behavior shown by the two mutant strains. In each case there are differences with wild-type mice in the usage of 8 specific syllables, but with the exception of one overlap, the affected syllables are distinct for the two genetic models.

该分析还提供了洞察力（可悲的令人沮丧）the drug risperidone which has shown efficacy against the CNTNAP-2 phenotype in mice它是一种经FDA批准的药物，用于治疗自闭症（精神分裂症和双相情感障碍的躁狂阶段）。在受CNTNAP2缺乏影响的8个音节中，只有一个受利培酮的影响，而其他正常音节也频繁使用的其他正常音节。结果表明，至少在小鼠中，该药物充当镇静剂，而不是纠正由突变引起的电路缺陷。

With a company spin-of  (音节生命科学），，，，exploitation of this new ability to decipher the body language of mice for therapeutic benefit is already underway, while for researchers Datta’s model is freely available ( an announcement greeted with spontaneous clapping by symposium attendees).

将行为与神经回路联系起来

There were many other presentations involving computational ethology at会议以及一些说明的说明，如何将自动跟踪与现代工具结合使用，以监视和操纵神经回路以洞悉神经电路功能。

在早期出版的作品from his time as a postdoc in Joshua Shaevitz’ lab at Princeton, and done in collaboration with Bill Bialek, Gordon Berman used unsupervised machine learning to probe the ground-based social behavior of fruit flies, identifying 117 stereotypic motifs, each lasting on average 0.21 seconds and linked by transitions of average duration 0.13 seconds.

Mathematical modeling of these data与当前的果蝇神经回路组织的模型保持一致，指示不同时间尺度上的分层组织。现在，伯曼（Berman）正在应对这些行为的神经指挥代码的挑战，即将其压缩到颈部降低轴突中（比大脑或身体其余部分的神经元数少100倍），与Josh Shaevitz，Jessica Cande，Gwyneth Card和David Stern合作，分析了光遗传学刺激这些轴突的行为效应。

Thanks to Janelia Farm’s mission to develop and share tools for the research community, there are 2,215 GAL4 Drosophila lines that can drive the cell-type specific expression of genetic constructs, such as the light- or heat-activatable channels used in opto- and thermogenetic experiments.

Brian Duistermars给出了应用这些技术的一个整洁的例子，Brian Duistermars一直跟踪和分析了男性水果苍蝇的威胁显示，将这种行为的控制固定到六个能够根据强度引导全部或部分的神经元的控制。它们受到刺激。

A broader, more systematic screen for altered behaviors after thermogenetic activation in 2,200 GAL4 lines was described by Kristin Branson. Her group has developedJAABA，，，，an interactive machine learning approach that incorporates an element of supervision from biologists in analyzing animal behavior, and used this to classify 20 different ground-based social behaviors in Drosophila, whose modification can be detected computationally.

这使得将高维行为数据与整个神经系统中特定神经元的操纵联系起来的大脑行为图的产生。尽管结果尚未发表，但project实验室视频中的功能（下图）和Janelia传统忠实，该小组正在研究通过A共享数据的方法browsable atlas of行为- 解剖图。

斑马鱼是模型有机体通过计算伦理学产生新见解的另一个例子。

Gonzalo de Polaviejo studies decision making by zebrafish in a social context, using hispublished Id tracker softwarethat reliably identifies individual fish as they interact. The fish engage in non-random social interactions from 5 days post-fertilization and develop their social behaviour while still transparent larvae, promising a useful model in which to look for neural activity correlates and test the effects of genetic mutations and drugs.

But mice remain the dominant model for biomedical research relevant to human disease.

戈登·伯曼（Gordon Berman）使用他的无监督的机器学习和建模方法来研究小鼠和人类以及果蝇的行为，并使他的可用的型号在Github上。Preliminary results on the behavior of mice in both social and nonsocial contexts, obtained in collaboration with RC Liu’s group were presented in this海报。

梅根·凯里（Megan Carey）已经发展机车to analyze the limb, head, and tail kinematics of freely walking mice and investigate the effects of perturbing cerebellar circuitry.

Azim Eiman uses unsupervised learning from machine vision to detect and reliably quantify subtle variations in reach and grasp behavior as he builds on his出版的作品要了解小脑电路如何与电动机命令相交以微调这些面向目标的运动。

亚当·汉特曼（Adam Hantman）采用了布兰森（Branson）开发的交互式机器学习方法，分析了运动皮层在控制头部固定小鼠中触及和掌握行为中的作用。他的publishedwork shows an impressive “pause and play” level of manipulation using optogenetics, and he is now looking to correlate the kinematics of the behavior with activity in the motor cortex and subcortical areas, a feat made possible by a large field of view (5mm) 2-photon mesoscope recently developed bySofroniew等人2016，可以通过单个神经元分辨率获得不同大脑区域的同时钙成像。

总体而言，信息是伦理学正在赶上并与监测和操纵神经电路活动的进步联系在一起。借助复杂的计算分析，我们开始理解行为语言，大脑的最终输出。