Long-range synchrony and emergence of reentry in neural networks

TL;DR

This study investigates how long-range neural synchrony arises in large cortical networks, revealing a transition from irregular synchrony to stable reentry patterns as network parameters change.

Contribution

It introduces an experimental model to monitor long-distance neural activity and demonstrates how the length scale influences the emergence of reentry and synchrony modes.

Findings

Distant neuronal populations can synchronize over centimeters.

Reentry propagation leads to stable, long-lasting neural patterns.

The mode of synchrony depends on the length scale relative to network size.

Abstract

Synchronization across long neural distances is a functionally important phenomenon. In order to access the mechanistic basis of long-range synchrony, we constructed an experimental model that enables monitoring of spiking activities over centimeter scale distances in large random networks of cortical neutrons. We show that the mode of synchrony over these distances depends upon a length scale, $\lambda$, which is the minimal path that activity should travel through before meeting its point of origin ready for reactivation. When $\lambda$ is experimentally made larger than the physical dimension of the network, distant neuronal populations operate synchronously, giving rise to irregularly occurring network-wide events that last hundreds of milliseconds to couple of seconds. In contrast, when $\lambda$ approaches the dimension of the network, a continuous self-sustained reentry propagation emerges, a regular dynamical mode that is marked by precise spatiotemporal patterns (`synfire chains') that may last many minutes. These results contribute to discussions on the origin of different modes of neural synchrony in normal and pathological conditions