Packetization improves communication efficiency in brain networks with rapid and cost-effective propagation strategies

Abstract

Computational studies in network neuroscience build models of communication dynamics in the con-nectome that help us understand the structure-function relationships of the brain. In these models, the dynamics of cortical signal transmission in brain networks are approximated with simple propagation strategies such as random walks and shortest path routing. Furthermore, the signal transmission dynamics in brain networks are associated with the switching architectures of engineered communication systems (e.g., message switching and packet switching). However, it has been unclear how propagation strategies and switching architectures are related in models of brain network communication. Here, we investigate the effects of the difference between packet switching and message switching (i.e., whether signals are packetized or not) on the transmission efficiency of the propagation strategies when simulating signal propagation in a macaque brain network. The results show that packetization decreases the efficiency of the random walk strategy and does not change the efficiency of the shortest path strategy, but increases the efficiency of more plausible strategies for brain networks that balance between communication speed and information cost. This finding suggests an advantage of packet-switched communication in the connectome and provides new insights into modeling the communication dynamics in brain networks.