The structure identification of feedforward neuronal network based on adaptive synchronization
The function of the neuronal network is neural code. In the network, neurons connect with each other by synapses. The stability of synaptic connections ensures the reliable transmission of spiking activity in the network, which is one of the key properties of candidate neural code. However, some nervous system diseases can lead to some synaptic connections lost stochastically in the neuronal network, which will disturb the reliability of transmission seriously. For studying the transmission feature of the potential neural code, it is necessary to detect whether there exist lost synapses and their position in the network. In this paper, a virtual network is built to identify the synaptic connection structure in the feedforward neuronal network. Through the adaptive estimation method, the variable connections in the virtual network detected the connected and unconnected synapses successfully in the feedforward neuronal network. Furthermore, our simulation results proved that the theoretical analysis is effective. This research provides a general method to detect the lost synapses in the feedforward neuronal network.
feedforward neuronal network synapse identification
Ming Xue Jiang Wang Chenhui Jia Bin Deng Xile Wei Yanqiu Che
School of Electrical and automation engineering Tianjin University Tianjin, P. R. China School of Automation and Electrical Engineering Tianjin University of Technology and Education Tianj
国际会议
2011 4th International Congress on Image and Signal Processing(第四届图像与信号处理国际学术会议 CISP 2011)
上海
英文
2535-2539
2011-10-15（万方平台首次上网日期，不代表论文的发表时间）