Classifying Neuronal Cell Types Based on Shared Electrophysiological Information from Humans and Mice.

IF 2.7 4区 医学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Neuroinformatics Pub Date : 2024-07-08 DOI:10.1007/s12021-024-09675-5
Ofek Ophir, Orit Shefi, Ofir Lindenbaum
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Abstract

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.

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根据人类和小鼠共享的电生理信息对神经元细胞类型进行分类
大脑是一个控制各种功能的复杂系统。它由大量细胞组成,这些细胞表现出不同的特征。要了解大脑在健康和疾病中的功能,对神经元进行准确分类至关重要。机器学习领域的最新进展为根据神经元的电生理活动对其进行分类提供了一种方法。本文介绍了一种深度学习框架,该框架可完全在此基础上对神经元进行分类。该框架使用来自艾伦细胞类型数据库的数据,该数据库包含从小鼠和人类单细胞记录中提取的生物特征调查。在联合模型的帮助下,来自这两个来源的共享信息被用于将神经元划分为广泛的类型。我们建立了一个精确的领域自适应模型,整合了小鼠和人类的电生理数据。此外,来自小鼠神经元的数据(也包括转基因小鼠品系的标签)也利用可解释的神经网络模型进一步划分为亚型。该框架在准确度和精确度方面提供了最先进的结果,同时还为预测提供了解释。
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来源期刊
Neuroinformatics
Neuroinformatics 医学-计算机:跨学科应用
CiteScore
6.00
自引率
6.70%
发文量
54
审稿时长
3 months
期刊介绍: Neuroinformatics publishes original articles and reviews with an emphasis on data structure and software tools related to analysis, modeling, integration, and sharing in all areas of neuroscience research. The editors particularly invite contributions on: (1) Theory and methodology, including discussions on ontologies, modeling approaches, database design, and meta-analyses; (2) Descriptions of developed databases and software tools, and of the methods for their distribution; (3) Relevant experimental results, such as reports accompanie by the release of massive data sets; (4) Computational simulations of models integrating and organizing complex data; and (5) Neuroengineering approaches, including hardware, robotics, and information theory studies.
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