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Neural arbors are Pareto optimal.

Arjun Chandrasekhar1,2, Saket Navlakha1,2

  • 11 Bioinformatics and Systems Biology Program , University of California , San Diego , UK.

Proceedings. Biological Sciences
|May 2, 2019
PubMed
Summary

Neural arbors (dendrites and axons) balance wiring cost and conduction delay, approaching Pareto optimality. This network design principle is also observed in plant structures, suggesting shared evolutionary strategies.

Keywords:
Pareto optimalitybiological networksgraph theoryneural arborsplant architectures

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Area of Science:

  • Neuroscience
  • Computational Biology
  • Network Science

Background:

  • Neural arbors, comprising dendrites and axons, function as complex graphs connecting neurons.
  • Key topological constraints include minimizing wiring cost and conduction delay, which present competing optimization objectives.

Purpose of the Study:

  • To evaluate how effectively neural arbors resolve the trade-off between wiring cost and conduction delay using Pareto optimality theory.
  • To develop and assess an algorithm for generating near-optimal arbors that balance these competing objectives.
  • To investigate the proximity of real neural arbors to Pareto optimality and explore their classification potential.

Main Methods:

  • Application of Pareto optimality theory to analyze network design trade-offs in neural arbors.
  • Development of a novel algorithm to generate arbors that near-optimally balance wiring cost and conduction delay.
  • Analysis of 14,145 neural arbors across diverse species, brain regions, and cell types.
  • Comparative analysis with chance and baseline models to assess Pareto optimality.
  • Investigation of arbor position on the Pareto front for classification purposes.

Main Results:

  • Neural arbors exhibit a significantly higher degree of Pareto optimality than predicted by chance or baseline models.
  • The developed algorithm demonstrates improved performance in balancing wiring cost and conduction delay compared to previous methods.
  • Arbor position on the Pareto front and distance to it can distinguish between different arbor types (e.g., axons vs. dendrites) and cell types.
  • Plant shoot architectures also display Pareto optimal characteristics, suggesting convergent network design principles.

Conclusions:

  • Neural arbors are highly optimized structures that near-optimally balance competing network design objectives.
  • Pareto optimality provides a powerful framework for understanding neural arbor structure and function.
  • Convergent evolution may favor Pareto optimal network designs across vastly different biological systems.
  • This framework offers new avenues for classifying neural arbors and understanding their functional roles.