Comparison between methods of tonometry: time for a change of approach.

ABSTRACT Collision dynamics in brain network communication have been little studied. We describe a novel interaction that shows how nonlinear collision rules can result in efficient activity dynamics on simulated mammal brain networks. We tested the effects of collisions in “information spreading” (IS) models in comparison to standard random walk (RW) models. Simulations employ synchronous agents on tracer-based mesoscale mammal connectomes at a range of signal loads. We find that RW routing models have high average activity, which increases substantially with load. Activity in RW models is densely distributed over nodes: a substantial fraction are highly active in a given time window, and this fraction increases with load. Surprisingly, while IS routing models make many more attempts to pass signals, they show lower net activity due to collisions compared to RW. Activity in IS increases relatively little over a wide range of loads. In addition, IS models have greater sparseness (which decreases slowly with load) compared to RW models. Results hold on two networks of the monkey cortex and one of the mouse whole-brain. We also find evidence that activity is lower and more sparse for empirical networks compared to degree-matched randomized networks in IS, suggesting network topology supports IS routing.