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Yan Hao
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Publisher: Journals Gateway
Network Neuroscience (2020) 4 (4): 1055–1071.
Published: 01 November 2020
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Signal interactions in brain network communication have been little studied. We describe how nonlinear collision rules on simulated mammal brain networks can result in sparse activity dynamics characteristic of mammalian neural systems. We tested the effects of collisions in “information spreading” (IS) routing models and in standard random walk (RW) routing models. Simulations employed synchronous agents on tracer-based mesoscale mammal connectomes at a range of signal loads. We find that RW models have high average activity that increases with load. Activity in RW models is also densely distributed over nodes: a substantial fraction is highly active in a given time window, and this fraction increases with load. Surprisingly, while IS models make many more attempts to pass signals, they show lower net activity due to collisions compared to RW, and activity in IS increases little as function of load. Activity in IS also shows greater sparseness than RW, and sparseness decreases slowly with load. 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 under IS, suggesting that brain network topology supports IS-like routing strategies. Author Summary How do mammal brains control the communication of signals across their entire network? A fundamental goal for any large-scale communication system is managing signal interactions. Yet because brain dynamics are nonlinear and emergent, signal interactions on brain networks have been little studied. Here we investigate two forms of nonlinear signal interaction on the mammal mesoscale connectome: collisions and duplication (redundancy). Using explicit numerical simulations on high-accuracy connectomes of the mouse and monkey, we find evidence that mammal brain networks operate efficiently under a routing strategy that employs destructive collisions and the redundant spread of information. In contrast, standard random walk strategies with the same collision rule are less efficient in terms of global activity and sparseness of activity. Comparisons to randomized networks support these findings.
Includes: Supplementary data