Network Properties Deduced from Time-Series Data Analysis of its Nodes: Application to Neuronal and Cardiac Cultures in vitro

• Main Authors: Hui-Wen Chen, 陳惠雯
• Other Authors: Pik-Yin Lai
• Format: Others
• Language: zh-TW
• Published: 2012
• Online Access: http://ndltd.ncl.edu.tw/handle/70955350127888479531

碩士 === 國立中央大學 === 物理研究所 === 100 === The dynamics in a network is important in nature, for example the dynamics in complex neural network. The neural cells are coupled by the connection of the network. There are two methods in my thesis to obtain the network information from time-series data of its nodes. The first is the method of scaling of noisy fluctuation. The second is the Granger causality analysis. In the first method, we get information on the relative node degree in the network. By Granger causality analysis, we get the causal relation between the nodes in the network. We implement these two method and tested using networks of coupled excitable FitzHugh-Nagumo (FHN) elements to mimic excitable networks. We then apply this method to analyse the data from two experiments. One is the Multi-Electrode-Arrays (MEA) experimental data which record the time-series data in cultured neurons in vitro. We want to study the change of network with time and two kinds of drags, the Bicuculline (BMI) and Glutamate. Bicuculline inhibit the inhibition of the mechanism of neuron, and Glutamate is neural transmitter. The other is the video image data which record the time series data in developing cardiac culture in vitro. Then we discussed the cell-cell interactions in these neural and cardiac cultures.