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  1. Article ; Online: Causal connectivity measures for pulse-output network reconstruction: Analysis and applications.

    Tian, Zhong-Qi K / Chen, Kai / Li, Songting / McLaughlin, David W / Zhou, Douglas

    Proceedings of the National Academy of Sciences of the United States of America

    2024  Volume 121, Issue 14, Page(s) e2305297121

    Abstract: The causal connectivity of a network is often inferred to understand network function. It is arguably acknowledged that the inferred causal connectivity relies on the causality measure one applies, and it may differ from the network's underlying ... ...

    Abstract The causal connectivity of a network is often inferred to understand network function. It is arguably acknowledged that the inferred causal connectivity relies on the causality measure one applies, and it may differ from the network's underlying structural connectivity. However, the interpretation of causal connectivity remains to be fully clarified, in particular, how causal connectivity depends on causality measures and how causal connectivity relates to structural connectivity. Here, we focus on nonlinear networks with pulse signals as measured output, e.g., neural networks with spike output, and address the above issues based on four commonly utilized causality measures, i.e., time-delayed correlation coefficient, time-delayed mutual information, Granger causality, and transfer entropy. We theoretically show how these causality measures are related to one another when applied to pulse signals. Taking a simulated Hodgkin-Huxley network and a real mouse brain network as two illustrative examples, we further verify the quantitative relations among the four causality measures and demonstrate that the causal connectivity inferred by any of the four well coincides with the underlying network structural connectivity, therefore illustrating a direct link between the causal and structural connectivity. We stress that the structural connectivity of pulse-output networks can be reconstructed pairwise without conditioning on the global information of all other nodes in a network, thus circumventing the curse of dimensionality. Our framework provides a practical and effective approach for pulse-output network reconstruction.
    Language English
    Publishing date 2024-03-29
    Publishing country United States
    Document type Journal Article
    ZDB-ID 209104-5
    ISSN 1091-6490 ; 0027-8424
    ISSN (online) 1091-6490
    ISSN 0027-8424
    DOI 10.1073/pnas.2305297121
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Book ; Online: Quantitative relations among causality measures with applications to nonlinear pulse-output network reconstruction

    Tian, Zhong-qi K. / Chen, Kai / Li, Songting / McLaughlin, David W. / Zhou, Douglas

    2021  

    Abstract: The causal connectivity of a network is often inferred to understand the network function. It is arguably acknowledged that the inferred causal connectivity relies on causality measure one applies, and it may differ from the network's underlying ... ...

    Abstract The causal connectivity of a network is often inferred to understand the network function. It is arguably acknowledged that the inferred causal connectivity relies on causality measure one applies, and it may differ from the network's underlying structural connectivity. However, the interpretation of causal connectivity remains to be fully clarified, in particular, how causal connectivity depends on causality measures and how causal connectivity relates to structural connectivity. Here, we focus on nonlinear networks with pulse signals as measured output, $e.g.$, neural networks with spike output, and address the above issues based on four intensively utilized causality measures, $i.e.$, time-delayed correlation, time-delayed mutual information, Granger causality, and transfer entropy. We theoretically show how these causality measures are related to one another when applied to pulse signals. Taking the simulated Hodgkin-Huxley neural network and the real mouse brain network as two illustrative examples, we further verify the quantitative relations among the four causality measures and demonstrate that the causal connectivity inferred by any of the four well coincides with the underlying network structural connectivity, therefore establishing a direct link between the causal and structural connectivity. We stress that the structural connectivity of networks can be reconstructed pairwisely without conditioning on the global information of all other nodes in a network, thus circumventing the curse of dimensionality. Our framework provides a practical and effective approach for pulse-output network reconstruction.
    Keywords Quantitative Biology - Neurons and Cognition
    Subject code 410 ; 000
    Publishing date 2021-10-17
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  3. Article: The Dynamics of Balanced Spiking Neuronal Networks Under Poisson Drive Is Not Chaotic.

    Gu, Qing-Long L / Tian, Zhong-Qi K / Kovačič, Gregor / Zhou, Douglas / Cai, David

    Frontiers in computational neuroscience

    2018  Volume 12, Page(s) 47

    Abstract: Some previous studies have shown that chaotic dynamics in the balanced state, i.e., one with balanced excitatory and inhibitory inputs into cortical neurons, is the underlying mechanism for the irregularity of neural activity. In this work, we focus on ... ...

    Abstract Some previous studies have shown that chaotic dynamics in the balanced state, i.e., one with balanced excitatory and inhibitory inputs into cortical neurons, is the underlying mechanism for the irregularity of neural activity. In this work, we focus on networks of current-based integrate-and-fire neurons with delta-pulse coupling. While we show that the balanced state robustly persists in this system within a broad range of parameters, we mathematically prove that the largest Lyapunov exponent of this type of neuronal networks is negative. Therefore, the irregular firing activity can exist in the system without the chaotic dynamics. That is the irregularity of balanced neuronal networks need not arise from chaos.
    Language English
    Publishing date 2018-06-28
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2452964-3
    ISSN 1662-5188
    ISSN 1662-5188
    DOI 10.3389/fncom.2018.00047
    Database MEDical Literature Analysis and Retrieval System OnLINE

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