TY - GEN
T1 - The structure identification of feedforward neuronal network based on adaptive synchronization
AU - Xue, Ming
AU - Wang, Jiang
AU - Jia, Chenhui
AU - Deng, Bin
AU - Wei, Xile
AU - Che, Yanqiu
PY - 2011
Y1 - 2011
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84862916009&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84862916009&partnerID=8YFLogxK
U2 - 10.1109/CISP.2011.6100687
DO - 10.1109/CISP.2011.6100687
M3 - Conference contribution
AN - SCOPUS:84862916009
SN - 9781424493067
T3 - Proceedings - 4th International Congress on Image and Signal Processing, CISP 2011
SP - 2508
EP - 2512
BT - Proceedings - 4th International Congress on Image and Signal Processing, CISP 2011
T2 - 4th International Congress on Image and Signal Processing, CISP 2011
Y2 - 15 October 2011 through 17 October 2011
ER -