# Neural Optimization: Understanding Trade-offs with Pareto Theory

arXiv: https://arxiv.org/abs/2105.01395 · HTML: https://mlsanity.com/paper/2105.01395/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 May 2021.

## Built on by (most cited first, 4 of 4)

- [QNAS: A Neural Architecture Search Framework for Accurate and Efficient Quantum Neural Networks](https://mlsanity.com/paper/2604.07013/) (2026)
- [Evolutionary Optimization Reveals Structural Constraints on Reservoir Architecture for Spatiotemporal Chaos](https://mlsanity.com/paper/2606.22765/) (2026)
- [Commuting Distance Regularization for Timescale-Dependent Label Inconsistency in EEG Emotion Recognition](https://mlsanity.com/paper/2507.10895/) (2025)
- [Normative Loss Landscape Navigation: A Trajectory-Based Approach to Mitigating Forgetting in Incremental Learning](https://mlsanity.com/paper/2609.35926/) (2026)

## Abstract (arXiv)

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.