Neuroscientists at the University of Pittsburgh have created a machine learning model to understand how brains recognize communication sounds. The model, tested on guinea pigs, accurately predicted brain activity in response to different sound categories. The research also revealed that guinea pigs
, neuroscientists at the University of Pittsburgh have developed a machine learning model to understand how brains, including those of marmoset monkeys and guinea pigs, recognize and categorize sounds such as mating, food, or danger calls. The researchers drew parallels between sound recognition and facial recognition, where instead of matching a perfect template, the brain recognizes specific features.
“More or less everyone we know will lose some of their hearing at some point in their lives, either as a result of aging or exposure to noise. Understanding the biology of sound recognition and finding ways to improve it is important,” said senior author and Pitt assistant professor of neurobiology Srivatsun Sadagopan, Ph.D. “But the process of vocal communication is fascinating in and of itself.
The team started with the intuition that the way the human brain recognizes and captures the meaning of communication sounds may be similar to how it recognizes faces compared with other objects. Faces are highly diverse but have some common characteristics. They then wanted to check the performance of the model against the real-life behavior of the animals.
Not only were the animals able to perform the task as consistently as if the calls they heard were unaltered, they continued to perform well despite artificial echoes or noise. Better yet, the machine learning model described their behavior perfectly.from animals into human speech.
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