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  Reverse Engineering the 3D Structure and Sensory-Evoked Signal Flow of Rat Vibrissal Cortex

Egger, R., Dercksen, V., Kock, C., & Oberlaender, M. (2014). Reverse Engineering the 3D Structure and Sensory-Evoked Signal Flow of Rat Vibrissal Cortex. In H. Cuntz, M. W. Remme, & B. Torben-Nielsen (Eds.), The Computing Dendrite (pp. 127-145). New York: Springer.

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0019-0E1F-3 Version Permalink: https://hdl.handle.net/11858/00-001M-0000-0028-294C-F
Genre: Book Chapter

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 Creators:
Egger, Robert, Author
Dercksen, VincentJ., Author
Kock, ChristiaanP.J., Author
Oberlaender, Marcel1, Author
Affiliations:
1Max Planck Florida Institute for Neuroscience, Max Planck Society, One Max Planck Way, Jupiter FL 33458, USA, ou_1950288              

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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: 2014-01-01
 Publication Status: Issued
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 Rev. Type: -
 Identifiers: URI: http://dx.doi.org/10.1007/978-1-4614-8094-5_8
ISBN: 978-1-4614-8093-8
 Degree: -

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Title: The Computing Dendrite
Source Genre: Book
 Creator(s):
Cuntz, Hermann, Editor
Remme, Michiel W.H., Editor
Torben-Nielsen, Benjamin, Editor
Affiliations:
-
Publ. Info: New York : 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
Source Genre: Series
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Publ. Info: -
Pages: - Volume / Issue: 11 Sequence Number: - Start / End Page: 127 - 145 Identifier: -