Measuring directed interactions using cellular neural networks with complex connection topologies

TL;DR

This paper explores how complex connection topologies in cellular neural networks can improve the analysis of directed interactions in complex systems, especially in neural dynamics related to seizures.

Contribution

It introduces complex topologies in CNNs, demonstrating faster network optimization and better approximation of directed interactions compared to traditional lattice-like structures.

Findings

Complex CNN topologies enable faster network optimization.

Improved accuracy in approximating directed interactions.

Application to seizure dynamics analysis.

Abstract

We advance our approach of analyzing the dynamics of interacting complex systems with the nonlinear dynamics of interacting nonlinear elements. We replace the widely used lattice-like connection topology of cellular neural networks (CNN) by complex topologies that include both short- and long-ranged connections. With an exemplary time-resolved analysis of asymmetric nonlinear interdependences between the seizure generating area and its immediate surrounding we provide first evidence for complex CNN connection topologies to allow for a faster network optimization together with an improved approximation accuracy of directed interactions.