Adaptive rewiring evolves brain-like structure in weighted networks

Ilias Rentzeperis,Cees Van Leeuwen

doi:10.1038/s41598-020-62204-7

Abstract

Activity-dependent plasticity refers to a range of mechanisms for adaptively reshaping neuronal connections. We model their common principle in terms of adaptive rewiring of network connectivity, while representing neural activity by diffusion on the network: Where diffusion is intensive, shortcut connections are established, while underused connections are pruned. In binary networks, this process is known to steer initially random networks robustly to high levels of structural complexity, reflecting the global characteristics of brain anatomy: modular or centralized small world topologies. We investigate whether this result extends to more realistic, weighted networks. Both normally- and lognormally-distributed weighted networks evolve either modular or centralized topologies. Which of these prevails depends on a single control parameter, representing global homeostatic or normalizing regulation mechanisms. Intermediate control parameter values exhibit the greatest levels of network complexity, incorporating both modular and centralized tendencies. The simulation results allow us to propose diffusion based adaptive rewiring as a parsimonious model for activity-dependent reshaping of brain connectivity structure.

Highlights

Activity-dependent plasticity refers to a range of mechanisms for adaptively reshaping neuronal connections
The pervasiveness of these properties raises the question whether they result from a common principle[5,8]. We proposed that these properties are the product of adaptive rewiring[9,10,11,12,13], for a review[14]
Whereas the mechanisms that shape the brain network show great variety, as they encompass brain growth[15], development, as well as learning16,for a review, they are alike in their common dependency on the network’s functional connectivity, i.e. the statistical dependencies between the nodes’ activities[12,17]. Adaptive rewiring formalizes this dependency in terms of graph theory, as it encompasses adding shortcut links to network regions with intense functional connectivity while pruning underused ones

Summary

Introduction

Activity-dependent plasticity refers to a range of mechanisms for adaptively reshaping neuronal connections. We model their common principle in terms of adaptive rewiring of network connectivity, while representing neural activity by diffusion on the network: Where diffusion is intensive, shortcut connections are established, while underused connections are pruned In binary networks, this process is known to steer initially random networks robustly to high levels of structural complexity, reflecting the global characteristics of brain anatomy: modular or centralized small world topologies. Whereas the mechanisms that shape the brain network show great variety, as they encompass brain growth[15], development, as well as learning16,for a review, they are alike in their common dependency on the network’s functional connectivity, i.e. the statistical dependencies between the nodes’ activities[12,17] Adaptive rewiring formalizes this dependency in terms of graph theory, as it encompasses adding shortcut links to network regions with intense functional connectivity while pruning underused ones. Robust convergence to small world topologies is not automatically www.nature.com/scientificreports guaranteed for adaptive rewiring of differentially weighted networks; rewiring of low-weight connections may not impact the network flow, whereas highly weighted ones may resist pruning[21]

Methods

Results

Discussion

Conclusion