Neural arbors are Pareto optimal

Chandrasekhar, A., Navlakha, S. (May 2019) Neural arbors are Pareto optimal. Proc Biol Sci, 286 (1902). p. 20182727. ISSN 0962-8452 (Public Dataset)

Abstract

Neural arbors (dendrites and axons) can be viewed as graphs connecting the cell body of a neuron to various pre- and post-synaptic partners. Several constraints have been proposed on the topology of these graphs, such as minimizing the amount of wire needed to construct the arbor (wiring cost), and minimizing the graph distances between the cell body and synaptic partners (conduction delay). These two objectives compete with each other-optimizing one results in poorer performance on the other. Here, we describe how well neural arbors resolve this network design trade-off using the theory of Pareto optimality. We develop an algorithm to generate arbors that near-optimally balance between these two objectives, and demonstrate that this algorithm improves over previous algorithms. We then use this algorithm to study how close neural arbors are to being Pareto optimal. Analysing 14 145 arbors across numerous brain regions, species and cell types, we find that neural arbors are much closer to being Pareto optimal than would be expected by chance and other reasonable baselines. We also investigate how the location of the arbor on the Pareto front, and the distance from the arbor to the Pareto front, can be used to classify between some arbor types (e.g. axons versus dendrites, or different cell types), highlighting a new potential connection between arbor structure and function. Finally, using this framework, we find that another biological branching structure-plant shoot architectures used to collect and distribute nutrients-are also Pareto optimal, suggesting shared principles of network design between two systems separated by millions of years of evolution.

Item Type:	Paper
Subjects:	bioinformatics bioinformatics > computational biology > algorithms organs, tissues, organelles, cell types and functions > tissues types and functions > axon > axon arbors organs, tissues, organelles, cell types and functions > organs types and functions > brain organs, tissues, organelles, cell types and functions > cell types and functions > cell types organs, tissues, organelles, cell types and functions > cell types and functions > cell types organs, tissues, organelles, cell types and functions > cell types and functions > cell types organs, tissues, organelles, cell types and functions > cell types and functions bioinformatics > computational biology organs, tissues, organelles, cell types and functions > cell types and functions > cell types > dendritic cells > dendritic arbors organs, tissues, organelles, cell types and functions > cell types and functions > cell types > dendritic cells > dendritic arbors organs, tissues, organelles, cell types and functions > cell types and functions > cell types > dendritic cells > dendritic arbors organs, tissues, organelles, cell types and functions > cell types and functions > cell types > dendritic cells organs, tissues, organelles, cell types and functions > cell types and functions > cell types > dendritic cells organs, tissues, organelles, cell types and functions > cell types and functions > cell types > dendritic cells organs, tissues, organelles, cell types and functions > organs types and functions organs, tissues, organelles, cell types and functions
CSHL Authors:	Navlakha, Saket
Communities:	CSHL labs > Navlakha lab
Depositing User:	Matthew Dunn
Date:	15 May 2019
Date Deposited:	06 Nov 2019 16:28
Last Modified:	01 Feb 2024 20:57
PMCID:	PMC6532510
Related URLs:	Publisher
Dataset ID:	Code https://github.com/arjunc12/neurons. Neural Arbor Tracings http://neuromorpho.org/ Suppement https://dx.doi.org/10.6084/m9.figshare.c.4472489
URI:	https://repository.cshl.edu/id/eprint/38639

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