Quantitative relations among causality measures with applications to nonlinear pulse-output network reconstruction

TL;DR

This paper investigates how different causality measures relate to each other and to the underlying structural connectivity in nonlinear pulse-output networks, providing a practical method for network reconstruction.

Contribution

It establishes theoretical relations among causality measures and demonstrates their effectiveness in reconstructing network structure from pulse signals.

Findings

Causality measures are quantitatively related when applied to pulse signals.

Causal connectivity inferred by these measures aligns with the network's structural connectivity.

The method allows pairwise network reconstruction without global information, avoiding the curse of dimensionality.

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.