text: 47,198,1506284963464990733,167708661,Peter Jedlicka,"Which ion channel parameters from a huge theoretically possible parameter space are present in real neurons? In our preprint, we propose that Pareto optimality can serve as a guiding principle for addressing this issue (known as ion channel degeneracy). <LINK> Pareto optimality could help find models with optimal ion channel configurations performing best for a trade-off between energy efficiency and functional effectiveness. This could reduce the high-dimensional parameter space to geometrically simple low-dimensional manifolds. Pareto optimality might provide insights into neuronal ion channel correlations (e.g., in Patch-seq data). Multi-objective Pareto optimality has been applied to biology by Uri Alon <LINK>. See also a great review by @DiesPallas et al. <LINK>",https://arxiv.org/abs/2203.06391,"Nerve cells encounter unavoidable evolutionary trade-offs between multiple tasks. They must consume as little energy as possible (be energy-efficient or economical) but at the same time fulfil their functions (be functionally effective). Neurons displaying best performance for such multi-task trade-offs are said to be Pareto optimal. However, it is not understood how ion channel parameters contribute to the Pareto optimal performance of neurons. Ion channel degeneracy implies that multiple combinations of ion channel parameters can lead to functionally similar neuronal behavior. Therefore, to simulate functional behavior, instead of a single model, neuroscientists often use populations of valid models with distinct ion conductance configurations. This approach is called population (also database or ensemble) modeling. It remains unclear, which ion channel parameters in a vast population of functional models are more likely to be found in the brain. Here we propose that Pareto optimality can serve as a guiding principle for addressing this issue. The Pareto optimum concept can help identify the subpopulations of conductance-based models with ion channel configurations that perform best for the trade-off between economy and functional effectiveness. In this way, the high-dimensional parameter space of neuronal models might be reduced to geometrically simple low-dimensional manifolds. Therefore, Pareto optimality is a promising framework for improving population modeling of neurons and their circuits. We also discuss how Pareto inference might help deduce neuronal functions from high-dimensional Patch-seq data. Furthermore, we hypothesize that Pareto optimality might contribute to our understanding of observed ion channel correlations in neurons. ","Pareto optimality, economy-effectiveness trade-offs and ion channel