Detecting cell assembly interaction patterns via Bayesian based change-point detection and graph inference model

Zhichao Lian, Xiang Li, Hongmiao Zhang, Hui Kuang, Kun Xie, Jianchuan Xing, Dajiang Zhu, Joseph Zhuo Tsien, Tianming Liu, Jing Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

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.

Original languageEnglish (US)
Title of host publication2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages17-20
Number of pages4
ISBN (Electronic)9781467319591
DOIs
StatePublished - Jul 29 2014
Event2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014 - Beijing, China
Duration: Apr 29 2014May 2 2014

Publication series

Name2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014

Other

Other2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014
CountryChina
CityBeijing
Period4/29/145/2/14

Keywords

  • Cell assebmly interaction
  • Neuronal code

ASJC Scopus subject areas

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

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    Lian, Z., Li, X., Zhang, H., Kuang, H., Xie, K., Xing, J., Zhu, D., Tsien, J. Z., Liu, T., & Zhang, J. (2014). Detecting cell assembly interaction patterns via Bayesian based change-point detection and graph inference model. In 2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014 (pp. 17-20). [6867798] (2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/isbi.2014.6867798