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dc.contributor.author
McAssey, Michael P.
dc.contributor.author
Bijma, Fetsje
dc.contributor.author
Tarigan, Bernadetta
dc.contributor.author
Pelt, Jaap van
dc.contributor.author
Ooyen, Arjen van
dc.contributor.author
Gunst, Mathisca de
dc.date.accessioned
2018-10-11T08:34:15Z
dc.date.available
2017-06-11T14:31:30Z
dc.date.available
2018-10-11T08:34:15Z
dc.date.issued
2014-01-29
dc.identifier.issn
1932-6203
dc.identifier.other
10.1371/journal.pone.0086526
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/94208
dc.identifier.doi
10.3929/ethz-b-000094208
dc.description.abstract
Neuronal signal integration and information processing in cortical neuronal networks critically depend on the organization of synaptic connectivity. Because of the challenges involved in measuring a large number of neurons, synaptic connectivity is difficult to determine experimentally. Current computational methods for estimating connectivity typically rely on the juxtaposition of experimentally available neurons and applying mathematical techniques to compute estimates of neural connectivity. However, since the number of available neurons is very limited, these connectivity estimates may be subject to large uncertainties. We use a morpho-density field approach applied to a vast ensemble of model-generated neurons. A morpho-density field (MDF) describes the distribution of neural mass in the space around the neural soma. The estimated axonal and dendritic MDFs are derived from 100,000 model neurons that are generated by a stochastic phenomenological model of neurite outgrowth. These MDFs are then used to estimate the connectivity between pairs of neurons as a function of their inter-soma displacement. Compared with other density-field methods, our approach to estimating synaptic connectivity uses fewer restricting assumptions and produces connectivity estimates with a lower standard deviation. An important requirement is that the model-generated neurons reflect accurately the morphology and variation in morphology of the experimental neurons used for optimizing the model parameters. As such, the method remains subject to the uncertainties caused by the limited number of neurons in the experimental data set and by the quality of the model and the assumptions used in creating the MDFs and in calculating estimating connectivity. In summary, MDFs are a powerful tool for visualizing the spatial distribution of axonal and dendritic densities, for estimating the number of potential synapses between neurons with low standard deviation, and for obtaining a greater understanding of the relationship between neural morphology and network connectivity.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
Public Library of Science
en_US
dc.rights.uri
http://creativecommons.org/licenses/by/4.0/
dc.title
A Morpho-Density Approach to Estimating Neural Connectivity
en_US
dc.type
Journal Article
dc.rights.license
Creative Commons Attribution 4.0 International
ethz.journal.title
PLoS ONE
ethz.journal.volume
9
en_US
ethz.journal.issue
1
en_US
ethz.journal.abbreviated
PLoS ONE
ethz.pages.start
e86526
en_US
ethz.size
11 p.
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.identifier.nebis
006206116
ethz.publication.place
S.l.
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich, direkt::00012 - Lehre und Forschung, direkt::00007 - Departemente, direkt::02120 - Departement Management, Technologie und Ökonomie / Department of Management, Technology, and Economics::03818 - Sutanto, Juliana (ehemalig)
en_US
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich, direkt::00012 - Lehre und Forschung, direkt::00007 - Departemente, direkt::02120 - Departement Management, Technologie und Ökonomie / Department of Management, Technology, and Economics::03818 - Sutanto, Juliana (ehemalig)
ethz.date.deposited
2017-06-11T14:32:01Z
ethz.source
ECIT
ethz.identifier.importid
imp593652a7a065446120
ethz.ecitpid
pub:148086
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2017-07-12T22:19:27Z
ethz.rosetta.lastUpdated
2018-10-11T08:34:24Z
ethz.rosetta.exportRequired
true
ethz.rosetta.versionExported
true
ethz.COinS
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