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 significant