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@ARTICLE{Butz:153449,
author = {Butz, Markus and Steenbuck, Ines D. and van Ooyen, Arjen},
title = {{H}omeostatic structural plasticity increases the
efficiency of small-world networks},
journal = {Frontiers in synaptic neuroscience},
volume = {6},
number = {7},
issn = {1663-3563},
address = {Lausanne},
publisher = {Frontiers Research Foundation},
reportid = {FZJ-2014-03056},
pages = {14},
year = {2014},
abstract = {In networks with small-world topology, which are
characterized by a high clustering coefficient and a short
characteristic path length, information can be transmitted
efficiently and at relatively low costs. The brain is
composed of small-world networks, and evolution may have
optimized brain connectivity for efficient information
processing. Despite many studies on the impact of topology
on information processing in neuronal networks, little is
known about the development of network topology and the
emergence of efficient small-world networks. We investigated
how a simple growth process that favors short-range
connections over long-range connections in combination with
a synapse formation rule that generates homeostasis in
post-synaptic firing rates shapes neuronal network topology.
Interestingly, we found that small-world networks benefited
from homeostasis by an increase in efficiency, defined as
the averaged inverse of the shortest paths through the
network. Efficiency particularly increased as small-world
networks approached the desired level of electrical
activity. Ultimately, homeostatic small-world networks
became almost as efficient as random networks. The increase
in efficiency was caused by the emergent property of the
homeostatic growth process that neurons started forming more
long-range connections, albeit at a low rate, when their
electrical activity was close to the homeostatic set-point.
Although global network topology continued to change when
neuronal activities were around the homeostatic equilibrium,
the small-world property of the network was maintained over
the entire course of development. Our results may help
understand how complex systems such as the brain could set
up an efficient network topology in a self-organizing
manner. Insights from our work may also lead to novel
techniques for constructing large-scale neuronal networks by
self-organization.},
cin = {JSC / JARA-HPC},
ddc = {610},
cid = {I:(DE-Juel1)JSC-20090406 / $I:(DE-82)080012_20140620$},
pnm = {411 - Computational Science and Mathematical Methods
(POF2-411) / SMHB - Supercomputing and Modelling for the
Human Brain (HGF-SMHB-2013-2017) / SLNS - SimLab
Neuroscience (Helmholtz-SLNS)},
pid = {G:(DE-HGF)POF2-411 / G:(DE-Juel1)HGF-SMHB-2013-2017 /
G:(DE-Juel1)Helmholtz-SLNS},
typ = {PUB:(DE-HGF)16},
pubmed = {pmid:24744727},
doi = {10.3389/fnsyn.2014.00007},
url = {https://juser.fz-juelich.de/record/153449},
}