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

Antón Sánchez, Laura and Effenberger, Félix and Bielza Lozoya, María Concepción and Larrañaga Múgica, Pedro María and Cuntz, Hermann (2018). A regularity index for dendrites - local statistics of a neuron's input space. "PLOS Computational Biology", v. 14 (n. 11); pp. 1-22. ISSN 1553-7358. https://doi.org/10.1371/journal.pcbi.1006593.

Description

Title: A regularity index for dendrites - local statistics of a neuron's input space
Author/s:
  • Antón Sánchez, Laura
  • Effenberger, Félix
  • Bielza Lozoya, María Concepción
  • Larrañaga Múgica, Pedro María
  • Cuntz, Hermann
Item Type: Article
Título de Revista/Publicación: PLOS Computational Biology
Date: 12 November 2018
ISSN: 1553-7358
Volume: 14
Subjects:
Faculty: E.T.S. de Ingenieros Informáticos (UPM)
Department: Inteligencia Artificial
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

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.

Funding Projects

TypeCodeAcronymLeaderTitle
Government of SpainC080020-09UnspecifiedUnspecifiedCajal Blue Brain
Government of SpainTIN2016-79684-PUnspecifiedUniversidad Politécnica de MadridAvances en clasificación multidimensional y detección de anomalías con redes bayesianas
Madrid Regional GovernmentS2013/ICE-2845CASI – CAMUnspecifiedConceptos y aplicaciones de los sistemas inteligentes
Horizon 2020785907HBP SGA2UnspecifiedHuman Brain Project Specific Grant Agreement 2

More information

Item ID: 54568
DC Identifier: http://oa.upm.es/54568/
OAI Identifier: oai:oa.upm.es:54568
DOI: 10.1371/journal.pcbi.1006593
Official URL: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1006593
Deposited by: Memoria Investigacion
Deposited on: 24 Apr 2019 08:54
Last Modified: 25 Apr 2019 11:35
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