Can multivariate Granger causality detect directed connectivity of a multistable and dynamic biological decision network model?

TL;DR

This paper demonstrates that multivariate Granger causality can effectively identify directed connectivity in complex, multistable biological neural networks, revealing causal influences during decision-making processes and differentiating correct from error decisions.

Contribution

The study applies time-domain multivariate Granger causality to a biologically based decision neural network, showing its effectiveness in uncovering causal connections in nonlinear, multistable systems.

Findings

MVGC reveals directed influences during decision-making.

Connectivity varies with decision accuracy.

Reconstructed connectivity aligns with the original model.

Abstract

Extracting causal connections can advance interpretable AI and machine learning. Granger causality (GC) is a robust statistical method for estimating directed influences (DC) between signals. While GC has been widely applied to analysing neuronal signals in biological neural networks and other domains, its application to complex, nonlinear, and multistable neural networks is less explored. In this study, we applied time-domain multi-variate Granger causality (MVGC) to the time series neural activity of all nodes in a trained multistable biologically based decision neural network model with real-time decision uncertainty monitoring. Our analysis demonstrated that challenging two-choice decisions, where input signals could be closely matched, and the appropriate application of fine-grained sliding time windows, could readily reveal the original model's DC. Furthermore, the identified DC varied based on whether the network had correct or error decisions. Integrating the identified DC from different decision outcomes recovered most of the original model's architecture, despite some spurious and missing connectivity. This approach could be used as an initial exploration to enhance the interpretability and transparency of dynamic multistable and nonlinear biological or AI systems by revealing causal connections throughout different phases of neural network dynamics and outcomes.