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@ARTICLE{ButzOstendorf:138398,
author = {Butz-Ostendorf, Markus and van Ooyen, Arjen},
title = {{A} {S}imple {R}ule for {D}endritic {S}pine and {A}xonal
{B}outon {F}ormation {C}an {A}ccount for {C}ortical
{R}eorganization after {F}ocal {R}etinal {L}esions},
journal = {PLoS Computational Biology},
volume = {9},
number = {10},
issn = {1553-734X},
address = {San Francisco, Calif.},
publisher = {Public Library of Science},
reportid = {FZJ-2013-04532},
pages = {e1003259},
year = {2013},
abstract = {Lasting alterations in sensory input trigger massive
structural and functional adaptations in cortical networks.
The principles governing these experience-dependent changes
are, however, poorly understood. Here, we examine whether a
simple rule based on the neurons’ need for homeostasis in
electrical activity may serve as driving force for cortical
reorganization. According to this rule, a neuron creates new
spines and boutons when its level of electrical activity is
below a homeostaticset-point and decreases the number of
spines and boutons when its activity exceeds this set-point.
In addition, neurons need a minimum level of activity to
form spines and boutons. Spine and bouton formation depends
solely on the neuron’s own activity level, and synapses
are formed by merging spines and boutons independently of
activity. Using a novel computational model, we show that
this simple growth rule produces neuron and network changes
as observed in thevisual cortex after focal retinal lesions.
In the model, as in the cortex, the turnover of dendritic
spines was increased strongest in the center of the lesion
projection zone, while axonal boutons displayed a marked
overshoot followed by pruning. Moreover, the decrease in
external input was compensated for by the formation of new
horizontal connections, which caused a retinotopic
remapping. Homeostatic regulation may provide a unifying
framework for understanding cortical reorganization,
including network repair in degenerative diseases or
following focal stroke.},
cin = {JSC / JARA-HPC},
ddc = {570},
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},
UT = {WOS:000330355300021},
pubmed = {pmid:24130472},
doi = {10.1371/journal.pcbi.1003259},
url = {https://juser.fz-juelich.de/record/138398},
}