期刊
出版社
SPRINGER HEIDELBERG
DOI: 10.1007/s00359-023-01664-4
关键词
Signal analysis; Artificial intelligence; Supervised learning; Chirping behavior; Weakly electric fish; Apteronotus leptorhynchus
Signal analysis is crucial in neuroethological research, but traditional methods are prone to investigator bias. In this study, the authors developed a supervised learning algorithm to detect subtypes of chirps in weakly electric fish. By employing artificial intelligence, they validated previous classifications of chirps and demonstrated the possibility of further differentiation into subtypes.
Signal analysis plays a preeminent role in neuroethological research. Traditionally, signal identification has been based on pre-defined signal (sub-)types, thus being subject to the investigator's bias. To address this deficiency, we have developed a supervised learning algorithm for the detection of subtypes of chirps-frequency/amplitude modulations of the electric organ discharge that are generated predominantly during electric interactions of individuals of the weakly electric fish Apteronotus leptorhynchus. This machine learning paradigm can learn, from a 'ground truth' data set, a function that assigns proper outputs (here: time instances of chirps and associated chirp types) to inputs (here: time-series frequency and amplitude data). By employing this artificial intelligence approach, we have validated previous classifications of chirps into different types and shown that further differentiation into subtypes is possible. This demonstration of its superiority compared to traditional methods might serve as proof-of-principle of the suitability of the supervised machine learning paradigm for a broad range of signals to be analyzed in neuroethology.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据