Transient to Zero-Lag Synchronization in Excitable Networks

TL;DR

This paper investigates how the transient time to achieve zero-lag synchronization in excitable neural networks depends on network topology features like diameter, circumference, and loop out-degree, revealing bounds from constant to quadratic scaling.

Contribution

It identifies key graph features that govern transient synchronization times and challenges the idea that neural network functionality depends only on sustained zero-lag synchronization.

Findings

Transient times are governed by network diameter, circumference, and loop out-degree.

Upper bounds on transient times range from O(1) to O(N^2).

Finite-time information can predict transient scaling.

Abstract

The scaling of transient times to zero-lag synchronization in networks composed of excitable units is shown to be governed by three features of the graph representing the network: the longest path between pairs of neurons (diameter), the largest loop (circumference) and the loop with the maximal average out degree. The upper bound of transient times can vary between O(1) and O(N2), where N is the size of the network, and its scaling can be predicted in many scenarios from finite time accumulated information of the transient. Results challenge the assumption that functionality of neural networks might depend solely upon the synchronized repeated activation such as zero-lag synchronization.