  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.
