Computational trade-offs as a core principle of brain function
Abstract: Prioritizing computational principles offers a promising strategy to link neural activity, computation, and behavior. In this talk, I will focus on computational trade-offs as a core principle of brain function. Pareto optimality posits that systems optimized for multiple, competing goals are constrained to a low-dimensional manifold, the Pareto front, that captures the trade-offs shaping their organization. I will present theory and data supporting the Pareto framework: First, we apply this framework to large-scale whole-brain functional data in humans (resting-state fMRI dataset, N=1200) and demonstrate that individual differences in the brain's functional connectome lie on a robust triangle. We show that this triangle, interpreted using network analysis, clinical data, and task performance, reflects fundamental information-processing trade-offs. Second, we show that the Pareto front is an efficient representation for task-related brain dynamics. Third, we characterize the constraints of a control mechanism on Pareto manifolds, suggest a potential representation for it, and infer its possible breakdown points. Finally, we show evidence from ADHD and Alzheimer's disease supporting these theoretical predictions. If time allows, I will briefly present how ASD variation can be cast as a computational trade-off between accurate encoding and fast adaptation. Together, these findings demonstrate that trade-offs can account for diverse patterns of neural function and dysfunction, underscoring the Pareto framework's role as a key computational principle for understanding brain and cognition.