Open AccessAn efficient method for compressing neuron morphology data
Author(s) -
Longfei Li
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1883/1/012126
Subject(s) - neuron , computer science , artificial neural network , coding (social sciences) , process (computing) , field (mathematics) , artificial intelligence , neuroscience , mathematics , psychology , statistics , pure mathematics , operating system
The human brain has a very complex network structure. In order to understand the information integration process of the neural network in the brain, the current research of computational neurology focuses on studying the laws of neuron morphology. In recent years, the reconstruction technology in the field has developed rapidly, and the production speed of neuron data has been greatly accelerated. The problem of neuron data management under the background of Big-Data has gradually emerged. This paper proposes a method for compressing neuron morphology data based on binary coding, so as to efficiently store neuron morphology data. Relevant experimental results prove that the method in this paper is widely applicable to various types and sizes of neuron data.