Laboratory of Neurobiology, Department of Biology, Northeastern University, 360 Huntington Ave, Boston, MA, 02115, USA.
J Comp Physiol A Neuroethol Sens Neural Behav Physiol. 2024 May;210(3):443-458. doi: 10.1007/s00359-023-01664-4. Epub 2023 Sep 13.
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.
信号分析在神经生态学研究中起着突出的作用。传统上，信号识别是基于预先定义的信号（子）类型的，因此受到研究人员的偏见。为了解决这个缺陷，我们开发了一种监督学习算法，用于检测电鳗放电的啁啾频率/幅度调制的亚类型，这些亚类型主要是在个体之间的电相互作用中产生的弱电鱼 Apteronotus leptorhynchus。这种机器学习范例可以从“真实数据”集中学习一个函数，该函数将适当的输出（此处：啁啾的时间实例和相关的啁啾类型）分配给输入（此处：时间序列频率和幅度数据）。通过采用这种人工智能方法，我们验证了以前对啁啾进行的不同类型的分类，并表明进一步进行亚型分化是可能的。与传统方法相比，该方法的优越性证明了监督机器学习范例适用于神经生态学中广泛的信号分析。
