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dc.contributor.authorTorben-Nielsen, Ben
dc.contributor.authorDe Schutter, Erik
dc.date.accessioned2016-03-08T11:43:23Z
dc.date.available2016-03-08T11:43:23Z
dc.date.issued2014
dc.identifier.citationTorben-Nielsen , B & De Schutter , E 2014 , ' Context-aware modeling of neuronal morphologies ' , Frontiers in Neuroanatomy , vol. 8 , 92 . https://doi.org/10.3389/fnana.2014.00092
dc.identifier.issn1662-5129
dc.identifier.urihttp://hdl.handle.net/2299/16734
dc.description© 2014 Torben-Nielsen and De Schutter. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms
dc.description.abstractNEURONAL MORPHOLOGIES ARE PIVOTAL FOR BRAIN FUNCTIONING: physical overlap between dendrites and axons constrain the circuit topology, and the precise shape and composition of dendrites determine the integration of inputs to produce an output signal. At the same time, morphologies are highly diverse and variant. The variance, presumably, originates from neurons developing in a densely packed brain substrate where they interact (e.g., repulsion or attraction) with other actors in this substrate. However, when studying neurons their context is never part of the analysis and they are treated as if they existed in isolation. Here we argue that to fully understand neuronal morphology and its variance it is important to consider neurons in relation to each other and to other actors in the surrounding brain substrate, i.e., their context. We propose a context-aware computational framework, NeuroMaC, in which large numbers of neurons can be grown simultaneously according to growth rules expressed in terms of interactions between the developing neuron and the surrounding brain substrate. As a proof of principle, we demonstrate that by using NeuroMaC we can generate accurate virtual morphologies of distinct classes both in isolation and as part of neuronal forests. Accuracy is validated against population statistics of experimentally reconstructed morphologies. We show that context-aware generation of neurons can explain characteristics of variation. Indeed, plausible variation is an inherent property of the morphologies generated by context-aware rules. We speculate about the applicability of this framework to investigate morphologies and circuits, to classify healthy and pathological morphologies, and to generate large quantities of morphologies for large-scale modeling.en
dc.format.extent14
dc.format.extent2621350
dc.language.isoeng
dc.relation.ispartofFrontiers in Neuroanatomy
dc.titleContext-aware modeling of neuronal morphologiesen
dc.contributor.institutionSchool of Computer Science
dc.description.statusPeer reviewed
rioxxterms.versionofrecord10.3389/fnana.2014.00092
rioxxterms.typeJournal Article/Review
herts.preservation.rarelyaccessedtrue


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