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The Morphological Identity of Insect Dendrites

Overview of attention for article published in PLoS Computational Biology, December 2008
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Title
The Morphological Identity of Insect Dendrites
Published in
PLoS Computational Biology, December 2008
DOI 10.1371/journal.pcbi.1000251
Pubmed ID
Authors

Hermann Cuntz, Friedrich Forstner, Juergen Haag, Alexander Borst

Abstract

Dendrite morphology, a neuron's anatomical fingerprint, is a neuroscientist's asset in unveiling organizational principles in the brain. However, the genetic program encoding the morphological identity of a single dendrite remains a mystery. In order to obtain a formal understanding of dendritic branching, we studied distributions of morphological parameters in a group of four individually identifiable neurons of the fly visual system. We found that parameters relating to the branching topology were similar throughout all cells. Only parameters relating to the area covered by the dendrite were cell type specific. With these areas, artificial dendrites were grown based on optimization principles minimizing the amount of wiring and maximizing synaptic democracy. Although the same branching rule was used for all cells, this yielded dendritic structures virtually indistinguishable from their real counterparts. From these principles we derived a fully-automated model-based neuron reconstruction procedure validating the artificial branching rule. In conclusion, we suggest that the genetic program implementing neuronal branching could be constant in all cells whereas the one responsible for the dendrite spanning field should be cell specific.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 124 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 8 6%
United States 4 3%
United Kingdom 3 2%
Greece 1 <1%
Canada 1 <1%
Unknown 107 86%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 41 33%
Researcher 28 23%
Student > Master 14 11%
Professor > Associate Professor 9 7%
Student > Doctoral Student 7 6%
Other 17 14%
Unknown 8 6%
Readers by discipline Count As %
Agricultural and Biological Sciences 65 52%
Neuroscience 20 16%
Computer Science 9 7%
Engineering 5 4%
Medicine and Dentistry 4 3%
Other 12 10%
Unknown 9 7%