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

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 e

SUBMITTER: Ophir O 

PROVIDER: S-EPMC11579117 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature