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'DeepSqueak' Helps Researchers Decode Rodent Chatter

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Mice and rats have a rich repertoire of around 20 kinds of calls.

Two scientists at the University of Washington School of Medicine have developed a software program that represents the first use of deep artificial neural networks in squeak detection.

Credit: Alice Gray

University of Washington (UW) School of Medicine researchers have developed a software program to identify and decode rodent vocalizations.

The DeepSqueak deep neural network converts audio signals into an image, or sonogram, which could be further refined with machine-vision algorithms developed for self-driving cars.

Said the UW School of Medicine's Russell Marx, "DeepSqueak uses biomimetic algorithms that learn to isolate vocalizations by being given labeled examples of vocalizations and noise."

According to co-developer Kevin Coffey, the program could distinguish between about 20 kinds of rodent calls.

The UW School of Medicine's John Neumaier said DeepSqueak should help his team develop treatments for alcohol or opioid withdrawal faster, and make ultrasonic vocalizations "convenient, affordable, and widely available."

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

From University of Washington
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Abstracts Copyright © 2019 SmithBucklin, Washington, DC, USA


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