Learning Shapes the Energy Cost of Neural Tasks

The stark difference in energy use between AI systems and biological brains highlights both the remarkable efficiency of the brain and our limited understanding of the energetics underlying neural computation. Although efficiency is widely cited as a principle of neural design, direct measurement of the energy cost of specific neural tasks within their corresponding circuits has remained limited. Here, by simultaneously measuring intracellular glucose and calcium dynamics in the same behaving mice in vivo, we use neuronal glucose consumption to define circuit-level energy costs associated with learning-based behavioral tasks. We found that the post-learning fuel cost per task was significantly lower than pre-learning levels across multiple hippocampus- and cortex-dependent learning models, with or without changes in bulk calcium dynamics. This change in fuel cost was not mediated by extracellular glucose transport but instead reflected a reduction in intracellular glucose consumption and depended on canonical plasticity mechanisms, including NMDAR signaling and protein synthesis. Together, these findings suggest that attenuation of task-specific energy cost may represent a general bioenergetic trajectory of learning and plasticity. This "energy minimization" hypothesis provides insight into how the biological brain achieves efficiency and offers an orthogonal, complementary perspective to neural activity-centered frameworks.