Neural Optimization: Understanding Trade-offs with Pareto Theory

TL;DR

This paper explores how Pareto theory can be used to analyze the trade-offs and optimality in neural systems, from cellular to network levels, considering multiple competing objectives.

Contribution

It introduces Pareto theory as a framework for understanding multi-objective optimization in neurobiological systems, highlighting its utility for analysis and hypothesis generation.

Findings

Pareto theory effectively characterizes trade-offs in neural systems.

The framework helps identify key objectives influencing neural design.

It offers a new perspective for analyzing neural optimality.

Abstract

Nervous systems, like any organismal structure, have been shaped by evolutionary processes to increase fitness. The resulting neural 'bauplan' has to account for multiple objectives simultaneously, including computational function as well as additional factors like robustness to environmental changes and energetic limitations. Oftentimes these objectives compete and a quantification of the relative impact of individual optimization targets is non-trivial. Pareto optimality offers a theoretical framework to decipher objectives and trade-offs between them. We, therefore, highlight Pareto theory as a useful tool for the analysis of neurobiological systems, from biophysically-detailed cells to large-scale network structures and behavior. The Pareto approach can help to assess optimality, identify relevant objectives and their respective impact, and formulate testable hypotheses.