Pareto optimality, economy-effectiveness trade-offs and ion channel degeneracy: Improving population models of neurons

TL;DR

This paper explores how Pareto optimality can improve population modeling of neurons by identifying ion channel configurations that balance energy efficiency and functional effectiveness, potentially reducing model complexity.

Contribution

It introduces Pareto optimality as a guiding principle for selecting neuron models, linking multi-task trade-offs to ion channel parameter distributions and neuronal behavior.

Findings

Pareto optimality can identify neuron subpopulations with optimal ion channel configurations.

Population models can be reduced to low-dimensional manifolds using Pareto principles.

The framework may help interpret ion channel correlations and neuronal functions from high-dimensional data.

Abstract

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.