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  Quantitative analysis of the spatial organization of synaptic inputs on the postsynaptic dendrite

Scheuss, V. (2018). Quantitative analysis of the spatial organization of synaptic inputs on the postsynaptic dendrite. Frontiers in Neural Circuits, 12: 39. doi:10.3389/fncir.2018.00039.

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Data_Sheet_1_QuantitativeAnalysisoftheSpatialOrganizationofSynapticInputsonthePostsynapticDendrite.PDF (Supplementary material), 73KB
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 Creators:
Scheuss, Volker1, Author              
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1Department: Synapses-Circuits-Plasticity / Bonhoeffer, MPI of Neurobiology, Max Planck Society, ou_1113545              

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Free keywords: NEURONS IN-VIVO; PRIMARY VISUAL-CORTEX; CORTICAL-NEURONS; PYRAMIDAL NEURONS; ACTIVE DENDRITES; CONNECTIVITY; SELECTIVITY; PLASTICITY; SPINES; INTEGRATIONNeurosciences & Neurology; synaptic input; dendritic integration; dendrite; spatial organization; synapse cluster; quantitative analysis;
 Abstract: The spatial organization of synaptic inputs on the dendritic tree of cortical neurons is considered to play an important role in the dendritic integration of synaptic activity. Active electrical properties of dendrites and mechanisms of dendritic integration have been studied for a long time. New technological developments are now enabling the characterization of the spatial organization of synaptic inputs on dendrites. However, quantitative methods for the analysis of such data are lacking. In order to place cluster parameters into the framework of dendritic integration and synaptic summation, these parameters need to be assessed rigorously in a quantitative manner. Here I present an approach for the analysis of synaptic input clusters on the dendritic tree that is based on combinatorial analysis of the likelihoods to observe specific input arrangements. This approach is superior to the commonly applied analysis of nearest neighbor distances between synaptic inputs comparing their distribution to simulations with random reshuffling or bootstrapping. First, the new approach yields exact likelihood values rather than approximate numbers obtained from simulations. Second and more importantly, the new approach identifies individual clusters and thereby allows to quantify and characterize individual cluster properties.

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Language(s): eng - English
 Dates: 2018-03-23
 Publication Status: Published in print
 Pages: 16
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: ISI: 000432878800001
DOI: 10.3389/fncir.2018.00039
 Degree: -

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Title: Frontiers in Neural Circuits
Source Genre: Journal
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Publ. Info: Lausanne : Frontiers Research Foundation
Pages: - Volume / Issue: 12 Sequence Number: 39 Start / End Page: - Identifier: ISSN: 1662-5110
CoNE: https://pure.mpg.de/cone/journals/resource/1662-5110