Metabolic cost as an organizing principle for cooperative learning

TL;DR

This paper proposes that metabolic costs serve as an organizing principle for cooperative neural learning, aligning neural actions with rewards and improving robustness of learning through energy constraints.

Contribution

It introduces a theoretical framework showing how metabolic costs can guide cooperative learning in neural populations, supported by two implementation models.

Findings

Metabolic constraints align neural actions with expected rewards.

Imposing metabolic costs enhances reward estimate accuracy.

Metabolically constrained models confirm theoretical predictions.

Abstract

This paper investigates how neurons can use metabolic cost to facilitate learning at a population level. Although decision-making by individual neurons has been extensively studied, questions regarding how neurons should behave to cooperate effectively remain largely unaddressed. Under assumptions that capture a few basic features of cortical neurons, we show that constraining reward maximization by metabolic cost aligns the information content of actions with their expected reward. Thus, metabolic cost provides a mechanism whereby neurons encode expected reward into their outputs. Further, aside from reducing energy expenditures, imposing a tight metabolic constraint also increases the accuracy of empirical estimates of rewards, increasing the robustness of distributed learning. Finally, we present two implementations of metabolically constrained learning that confirm our theoretical finding. These results suggest that metabolic cost may be an organizing principle underlying the neural code, and may also provide a useful guide to the design and analysis of other cooperating populations.