For many years, the researchers knew that rodents' squeaks tell a lot about how the animals are feeling, Much like a wagging tail on a dog, certain vocalizations indicate the rodents are happy. Conversely, other vocalizations indicate the rodents are stressed, or even depressed.

但为什么他们感兴趣的啮齿动物的情绪?这些研究人员想了解啮齿动物对不同刺激的反应。这可以帮助研究人员确定最好的方法帮助人们上瘾或沮丧。他们可以告诉如果一个治疗有助于降低抑郁症的感受通过简单分析如何啮齿动物聊天。

Rat chatter is about to decode since rodents communicate largely in ultrasonic vocalizations (USVs) that the human ears always hear. USVs range from 20 kHz to 115 kHz, while humans can typically hear a contributor from 20 Hz to 20 kHz.

到目前为止,研究人员已经严重依赖费时,手工分析的啮齿动物喋喋不休。如此高频率的声音,研究人员必须减缓录音以听到他们。即使专业麦克风,标签和分类的高音尖叫录音是劳动密集型的。这些方法也容易受到人为错误和误解。

“在过去,研究人员记录了这些获得更好的洞察动物行为测试期间的情绪状态,”约翰Neumaier博士,美国精神病学和行为科学教授,告诉Digital Trends。”问题是手动分析这些录音可以花十倍的时间听当减缓人类能听到的频率。这使工作负载详尽和气馁的研究人员利用这种自然宣读关于动物的情绪状态”。

因此,华盛顿大学的研究小组转向人工智能(AI)自动化这个过程。他们的程序叫做DeepSqueak,因为它依赖于人工智能被称为深度学习的一种形式。

使用深度学习分析usv

两位研究者,罗素马克思,一个技术人员在华盛顿大学的精神病学和行为科学和凯文·科菲博士,华盛顿大学的博士后研究员,曾与教授Neumaier创建DeepSqueak usv检测和分析的软件。他们的研究最近发表在《The Nature of Neuropsychopharmacology。

"We can train the software to analyze these calls in a way that is much more similar to how humans learn," said Coffey. "Rather than mathematically describing what a vocalization is, We just show it pictures and examples."

DeepSqueak works by turning The an audio problem into a visual problem. The input to DeepSqueak is an audio file. WAV or. (FLAC). DeepSqueak splits The audio files into short segments and then converts these segments into images (sonograms). The figure below shows The transformation from a raw audio file to a filtered sonogram.

The sonograms are fed into a deep learning AI program that identifies and classifies The images, much like The AI, informs in self - driving cars to identify The stop signs and lane markers. It first decides if a squeak is present in The sonogram, and if so, what type of squeak It is.

"DeepSqueak USES biomimetic algorithms that learn to isolate vocalizations by being given labeled examples of vocalizations and noise," said Marx.

The team started DeepSqueak using example code,Object Detection Using Faster - R - CNN Deep Learning从MathWorks网站。从那里,他们开发了DeepSqueak软件包,在MATLAB GUI。DeepSqueak使用计算机视觉系统工具箱,曲线拟合工具箱,图像处理工具箱,并行计算工具箱,深度学习工具箱。

Technology can help develop better treatments for addiction

该研究小组专注于精神病学和行为科学。这种非侵入性研究发现啮齿动物是最幸福期待奖励时,如糖,或玩同行。他们还发现当女性啮齿动物雄性啮齿动物表现不同。这是意料之中的。

Neumaier教授说,他的目标是开发治疗压力失调和上瘾。DeepSqueak将帮助实验室更快到达那里通过破译超声叫声方便快捷。

"If scientists can understand better how drugs change brain activity to cause pleasure or unpleasant feelings, we could devise better treatments for addiction," he said.

The team has made DeepSqueak available to all researchers so they can create their own analysis. The code is onmaking. The program can currently identify approximately 20 marketers USVs. The team hopes, that The as others identify and tag various USVs, they 'll be able to create a virtual Google Translate for rat chatter.

To learn more about DeepSqueak, check out this video: