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.
Highlights1. Nervous systems are not only optimized for function but also robustness, efficiency, and flexibility.Depending on the ecological niche, more than one constraint leaves its imprint onto the neural structure.2. The concept of Pareto optimality allows us to measure the influence of individual constraints on nervous system optimization. Neural design can be optimal along so-called Pareto boundaries, from electrical properties and cellular morphology to behavior.3. Pareto boundaries can be identified via parameter exploration in mathematical models or by extraction of correlations in experimental data that offer sufficient statistics on biological traits.4. The importance of the functional readout (e.g. information processed in sensory systems, robustness of motor output) determines the acceptable investment in other constraints.