TY - GEN
T1 - Detecting cell assembly interaction patterns via Bayesian based change-point detection and graph inference model
AU - Lian, Zhichao
AU - Li, Xiang
AU - Zhang, Hongmiao
AU - Kuang, Hui
AU - Xie, Kun
AU - Xing, Jianchuan
AU - Zhu, Dajiang
AU - Tsien, Joseph Zhuo
AU - Liu, Tianming
AU - Zhang, Jing
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014/7/29
Y1 - 2014/7/29
N2 - Recent studies have proposed the theory of functional network-level neural cell assemblies and their hierarchical organization architecture. In this study, we first proposed a novel Bayesian binary connectivity change point model to be applied on the binary spiking time series recorded from multiple neurons in the mouse hippocampus during three different emotional events, to find stable temporal segments of neural activity. We then applied a Bayesian graph inference algorithm on the segmentation results to find multiple functional interaction patterns underlying each experience. The resulting interaction patterns were analyzed by multi-view co-training method to identify the common sub-network structure of cell assemblies which are strongly connected i.e. "neural cliques". By analyzing the resulting sub-networks from three memory-producing events, it is found that there exist certain common neurons participating in the functional interactions across different events, lending strong support evidence to the hypothesis of hierarchical organization architecture of neuronal assemblies.
AB - Recent studies have proposed the theory of functional network-level neural cell assemblies and their hierarchical organization architecture. In this study, we first proposed a novel Bayesian binary connectivity change point model to be applied on the binary spiking time series recorded from multiple neurons in the mouse hippocampus during three different emotional events, to find stable temporal segments of neural activity. We then applied a Bayesian graph inference algorithm on the segmentation results to find multiple functional interaction patterns underlying each experience. The resulting interaction patterns were analyzed by multi-view co-training method to identify the common sub-network structure of cell assemblies which are strongly connected i.e. "neural cliques". By analyzing the resulting sub-networks from three memory-producing events, it is found that there exist certain common neurons participating in the functional interactions across different events, lending strong support evidence to the hypothesis of hierarchical organization architecture of neuronal assemblies.
KW - Cell assebmly interaction
KW - Neuronal code
UR - http://www.scopus.com/inward/record.url?scp=84927917986&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84927917986&partnerID=8YFLogxK
U2 - 10.1109/isbi.2014.6867798
DO - 10.1109/isbi.2014.6867798
M3 - Conference contribution
AN - SCOPUS:84927917986
T3 - 2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014
SP - 17
EP - 20
BT - 2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014
Y2 - 29 April 2014 through 2 May 2014
ER -