Analysis of Neural Oscillations on Drosophila’s Subesophageal Ganglion Based on Approximate Entropy

Entropy

Tian Mei⁵  Jingda Qiao²  Yi Zhou⁵  Huaiyu Gu²  Ziyi Chen¹  Xianghua Tian⁴  Kuiying Gu³ 

[1] Department of Neurology, First Affiliated Hospital of Sun Yat-sen University, Guangzhou 510080, China; E-Mail:;Department of Anatomy and Neurobiology, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou 510080, China; E-Mails:;College of Public Health, Xinjiang Medical University, Urumqi 830011, China; E-Mail:;College of Medical Engineering and Technology, Xinjiang Medical University, Urumqi 830011, China; E-Mail:;Department of Biomedical Engineering, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510080, China; E-Mail:

关键词: Drosophila;    oscillation;    approximate entropy;    subesophageal ganglion;    clustering;    dopamine;   

DOI  :  10.3390/e17106854

来源: mdpi

【 摘 要 】

The suboesophageal ganglion (SOG), which connects to both central and peripheral nerves, is the primary taste-processing center in the Drosophila’s brain. The neural oscillation in this center may be of great research value yet it is rarely reported. This work aims to determine the amount of unique information contained within oscillations of the SOG and describe the variability of these patterns. The approximate entropy (ApEn) values of the spontaneous membrane potential (sMP) of SOG neurons were calculated in this paper. The arithmetic mean (MA), standard deviation (SDA) and the coefficient of variation (CVA) of ApEn were proposed as the three statistical indicators to describe the irregularity and complexity of oscillations. The hierarchical clustering method was used to classify them. As a result, the oscillations in SOG were divided into five categories, including: (1) Continuous spike pattern; (2) Mixed oscillation pattern; (3) Spikelet pattern; (4) Busting pattern and (5) Sparse spike pattern. Steady oscillation state has a low level of irregularity, and vice versa. The dopamine stimulation can distinctly cut down the complexity of the mixed oscillation pattern. The current study provides a quantitative method and some critera on mining the information carried in neural oscillations.

【 授权许可】

CC BY   
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