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@ARTICLE{Duarte:862792,
author = {Duarte, Renato and Morrison, Abigail},
title = {{L}everaging heterogeneity for neural computation with
fading memory in layer 2/3 cortical microcircuits},
journal = {PLoS Computational Biology},
volume = {15},
number = {4},
issn = {1553-7358},
address = {San Francisco, Calif.},
publisher = {Public Library of Science},
reportid = {FZJ-2019-03013},
pages = {e1006781 -},
year = {2019},
abstract = {Complexity and heterogeneity are intrinsic to
neurobiological systems, manifest in every process, at every
scale, and are inextricably linked to the systems’
emergent collective behaviours and function. However, the
majority of studies addressing the dynamics and
computational properties of biologically inspired cortical
microcircuits tend to assume (often for the sake of
analytical tractability) a great degree of homogeneity in
both neuronal and synaptic/connectivity parameters. While
simplification and reductionism are necessary to understand
the brain’s functional principles, disregarding the
existence of the multiple heterogeneities in the cortical
composition, which may be at the core of its computational
proficiency, will inevitably fail to account for important
phenomena and limit the scope and generalizability of
cortical models. We address these issues by studying the
individual and composite functional roles of heterogeneities
in neuronal, synaptic and structural properties in a
biophysically plausible layer 2/3 microcircuit model, built
and constrained by multiple sources of empirical data. This
approach was made possible by the emergence of large-scale,
well curated databases, as well as the substantial
improvements in experimental methodologies achieved over the
last few years. Our results show that variability in single
neuron parameters is the dominant source of functional
specialization, leading to highly proficient microcircuits
with much higher computational power than their homogeneous
counterparts. We further show that fully heterogeneous
circuits, which are closest to the biophysical reality, owe
their response properties to the differential contribution
of different sources of heterogeneity.},
cin = {INM-6 / IAS-6 / INM-10 / JARA-HPC},
ddc = {610},
cid = {I:(DE-Juel1)INM-6-20090406 / I:(DE-Juel1)IAS-6-20130828 /
I:(DE-Juel1)INM-10-20170113 / $I:(DE-82)080012_20140620$},
pnm = {574 - Theory, modelling and simulation (POF3-574) /
W2Morrison - W2/W3 Professorinnen Programm der
Helmholtzgemeinschaft (B1175.01.12) / SMHB - Supercomputing
and Modelling for the Human Brain (HGF-SMHB-2013-2017) /
Functional Neural Architectures $(jinm60_20190501)$},
pid = {G:(DE-HGF)POF3-574 / G:(DE-HGF)B1175.01.12 /
G:(DE-Juel1)HGF-SMHB-2013-2017 /
$G:(DE-Juel1)jinm60_20190501$},
typ = {PUB:(DE-HGF)16},
pubmed = {pmid:31022182},
UT = {WOS:000467530600025},
doi = {10.1371/journal.pcbi.1006781},
url = {https://juser.fz-juelich.de/record/862792},
}