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  Reverse engineering the 3D structure and sensory-evoked signal flow of rat vibrissal cortex

Egger, R., Dercksen, V., De Kock, C., & Oberlaender, M. (2014). Reverse engineering the 3D structure and sensory-evoked signal flow of rat vibrissal cortex. In H. Cuntz, & M. Remme (Eds.), The Computing Dendrite: form Structure to Function (pp. 127-145). New York, NY, USA: Springer.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0001-2F14-5 Version Permalink: http://hdl.handle.net/21.11116/0000-0003-671B-C
Genre: Book Chapter

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 Creators:
Egger, R1, 2, Author              
Dercksen, VJ, Author
De Kock, CPJ, Author
Oberlaender, M1, 2, Author              
Affiliations:
1Former Research Group Computational Neuroanatomy, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_2528698              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: Soma location, dendrite morphology, and synaptic innervation are key determinants of neuronal function. Unfortunately, conventional functional measurements of sensory-evoked activity in vivo yield limited structural information. In particular, when trying to infer mechanistic principles that underlie perception and behavior, interpretations from functional recordings of individual or small groups of neurons often remain ambiguous without detailed knowledge of the underlying network structures. Here we review a novel reverse engineering approach that allows investigating sensory-evoked signal flow through individual and ensembles of neurons within the context of their surrounding neural networks. To do so, spontaneous and sensory-evoked activity patterns are recorded from individual neurons in vivo. In addition, the complete 3D dendrite and axon projection patterns of such in vivo-characterized neurons are reconstructed and integrated into an anatomically realistic model of the rat vibrissal cortex. This model allows estimating the number and cell type-specific subcellular distribution of synapses on these neurons with 50 μm precision. As a result, each neuron can be described by a rich set of parameters that allows investigating structure–function relationships and simulation experiments at single-neuron and network levels.

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 Dates: 2014
 Publication Status: Published in print
 Pages: -
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 Rev. Method: Peer
 Identifiers: DOI: 10.1007/978-1-4614-8094-5_8
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Title: The Computing Dendrite: form Structure to Function
Source Genre: Book
 Creator(s):
Cuntz, H, Editor
Remme, MWH, Editor
Torben-Nielsen, B, Author
Affiliations:
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Publ. Info: New York, NY, USA : Springer
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 127 - 145 Identifier: ISBN: 978-1-4614-8093-8

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Title: Springer Series in Computational Neuroscience book series
Source Genre: Series
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Pages: - Volume / Issue: 11 Sequence Number: - Start / End Page: - Identifier: -