Synchronization is a fundamental property of biological neural networks, playing a mechanistic role in both healthy and disease brain states. The medullary pacemaker nucleus of the weakly electric fish is a synchronized network of high-frequency neurons, weakly coupled via gap junctions. Synchrony in the pacemaker is behaviourally modulated on millisecond timescales, but how gap junctional connectivity enables such rapid resynchronization speeds is poorly understood. Here, we use a computational model of the pacemaker, along with graph theory and predictive analyses, to investigate how network properties, such as randomness and the directionality of coupling (bidirectional/non-rectifying versus directional/rectifying gap junctions) characterize the fast synchronization of the pacemaker network. Our results provide predictions about connectivity in the pacemaker and insight into the relationship between structural network properties and synchronization dynamics in neural systems more generally.
bioRxiv Subject Collection: Neuroscience