  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.
