Faculty of Engineering, Bar-Ilan University, Ramat-Gan, Israel.
The Leslie & Susan Goldschmied (Gonda) Multidisciplinary Brain Research Center, Bar-Ilan University, Ramat-Gan, Israel.
Neuroinformatics. 2024 Oct;22(4):473-486. doi: 10.1007/s12021-024-09675-5. Epub 2024 Jul 8.
The brain is an intricate system that controls a variety of functions. It consists of a vast number of cells that exhibit diverse characteristics. To understand brain function in health and disease, it is crucial to classify neurons accurately. Recent advancements in machine learning have provided a way to classify neurons based on their electrophysiological activity. This paper presents a deep-learning framework that classifies neurons solely on this basis. The framework uses data from the Allen Cell Types database, which contains a survey of biological features derived from single-cell recordings from mice and humans. The shared information from both sources is used to classify neurons into their broad types with the help of a joint model. An accurate domain-adaptive model, integrating electrophysiological data from both mice and humans, is implemented. Furthermore, data from mouse neurons, which also includes labels of transgenic mouse lines, is further classified into subtypes using an interpretable neural network model. The framework provides state-of-the-art results in terms of accuracy and precision while also providing explanations for the predictions.
大脑是一个复杂的系统，控制着各种功能。它由大量具有不同特征的细胞组成。为了了解健康和疾病中的大脑功能，准确地对神经元进行分类至关重要。最近的机器学习进展为基于其电生理活动对神经元进行分类提供了一种方法。本文提出了一种仅基于此基础对神经元进行分类的深度学习框架。该框架使用来自 Allen 细胞类型数据库的数据，其中包含从小鼠和人类的单细胞记录中得出的生物特征调查。借助联合模型，将来自两个来源的共享信息用于将神经元分类为其广泛类型。实现了一个准确的域自适应模型，该模型整合了来自小鼠和人类的电生理数据。此外，还使用可解释的神经网络模型对包含转基因小鼠系标签的小鼠神经元数据进行进一步的亚型分类。该框架在准确性和精度方面提供了最先进的结果，同时还为预测提供了解释。
