Synchronization malleability in neural networks under a distance-dependent coupling

TL;DR

This paper explores how the synchronization patterns in a neural network with distance-dependent coupling can vary significantly based on input ordering, revealing a phenomenon called synchronization malleability.

Contribution

It introduces the concept of synchronization malleability in neural networks and analyzes how input ordering affects synchronization structures under a power-law coupling model.

Findings

Synchronization patterns depend on input ordering.

Synchronization malleability occurs in specific parameter regions.

Functional connectivity varies with input sequence.

Abstract

We investigate the synchronization features of a network of spiking neurons under a distance-dependent coupling following a power-law model. The interplay between topology and coupling strength leads to the existence of different spatiotemporal patterns, corresponding to either non-synchronized or phase-synchronized states. Particularly interesting is what we call synchronization malleability, in which the system depicts significantly different phase synchronization degrees for the same parameters as a consequence of a different ordering of neural inputs. We analyze the functional connectivity of the network by calculating the mutual information between neuronal spike trains, allowing us to characterize the structures of synchronization in the network. We show that these structures are dependent on the ordering of the inputs for the parameter regions where the network presents synchronization malleability and we suggest that this is due to a complex interplay between coupling, connection architecture, and individual neural inputs.