Information, Mass, Energy Principles: A biophysical and ecological framework for understanding the brain

The brain is a highly complex biological system whose structure, function, and evolution are constrained by material resources and energy availability, as any other natural system. Here, we extend the ecological Information-Mass-Energy (IME) framework (Peñuelas, 2024) to neuroscience. In contrast to purely computational models, we propose that the brain is an information-organizing system constrained by mass availability, energetic expenditure, and thermodynamic efficiency. Through comparative analyses across mammalian species, we identify a superlinear scaling relationship between white and grey matter volumes, alongside a positive association between white matter volume and inhibitory-control performance. This suggests that neural organization contribute to cognitive capacities beyond brain size alone. Integrating these findings with metabolic scaling principles, we propose that brain evolution involves trade-offs between increasing informational complexity and the material and energetic costs required to sustain it. Within this framework, we formulate testable hypotheses concerning how informational organization may scale with neural material and energetic expenditure. Furthermore, we introduce a quantitative disturbance-recovery model that incorporates carrying capacity, recovery rate, and disturbance magnitude and duration, generating testable predictions for neurological conditions such as stroke and neurodegeneration. By combining empirical scaling relationships with a mathematical framework for neural organization, energetic constraints, and resilience, the IME framework offers a novel biophysical and ecological perspective on brain evolution and function. This framework also provides a foundation for investigating energetically sustainable information processing in biological and artificial systems.