A regularity index for dendrites - local statistics of a neuron's input space

  • Neurons collect their inputs from other neurons by sending out arborized dendritic structures. However, the relationship between the shape of dendrites and the precise organization of synaptic inputs in the neural tissue remains unclear. Inputs could be distributed in tight clusters, entirely randomly or else in a regular grid-like manner. Here, we analyze dendritic branching structures using a regularity index R, based on average nearest neighbor distances between branch and termination points, characterizing their spatial distribution. We find that the distributions of these points depend strongly on cell types, indicating possible fundamental differences in synaptic input organization. Moreover, R is independent of cell size and we find that it is only weakly correlated with other branching statistics, suggesting that it might reflect features of dendritic morphology that are not captured by commonly studied branching statistics. We then use morphological models based on optimal wiring principles to study the relation between input distributions and dendritic branching structures. Using our models, we find that branch point distributions correlate more closely with the input distributions while termination points in dendrites are generally spread out more randomly with a close to uniform distribution. We validate these model predictions with connectome data. Finally, we find that in spatial input distributions with increasing regularity, characteristic scaling relationships between branching features are altered significantly. In summary, we conclude that local statistics of input distributions and dendrite morphology depend on each other leading to potentially cell type specific branching features.
Metadaten
Author:Laura Anton-Sanchez, Felix Effenberger, Concha Bielza, Pedro Larrañaga, Hermann CuntzORCiDGND
URN:urn:nbn:de:hebis:30:3-478168
DOI:https://doi.org/10.1371/journal.pcbi.1006593
ISSN:1553-7358
ISSN:1553-734X
Pubmed Id:https://pubmed.ncbi.nlm.nih.gov/30419016
Parent Title (English):PLoS Computational Biology
Publisher:Public Library of Science
Place of publication:San Francisco, Calif.
Contributor(s):Abigail Morrison
Document Type:Article
Language:English
Year of Completion:2018
Date of first Publication:2018/11/12
Publishing Institution:Universitätsbibliothek Johann Christian Senckenberg
Release Date:2018/11/27
Tag:Connectomics; Dendritic structure; Monte Carlo method; Neuronal dendrites; Neurons; Pyramidal cells; Statistical distributions; Synapses
Volume:14
Issue:(11): e1006593
Page Number:22
First Page:1
Last Page:22
Note:
Copyright: © 2018 Anton-Sanchez et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
HeBIS-PPN:441017002
Institutes:Biowissenschaften / Biowissenschaften
Wissenschaftliche Zentren und koordinierte Programme / Frankfurt Institute for Advanced Studies (FIAS)
Dewey Decimal Classification:5 Naturwissenschaften und Mathematik / 57 Biowissenschaften; Biologie / 570 Biowissenschaften; Biologie
Sammlungen:Universitätspublikationen
Licence (German):License LogoCreative Commons - Namensnennung 4.0