Understanding how neurons arrange themselves into neural networks that result in behaviors is a major goal in neuroscience. Most theoretical and experimental efforts have focused on a top-down approach which seeks to identify neuronal correlates of behaviors. This has been accomplished by effectively mapping specific behaviors to distinct neural patterns or by creating computational models that produce a desired behavioral outcome. Nonetheless, these approaches, have only implicitly considered the fact that neural tissue, like any other physical system, is subjected to several restrictions and boundaries of operations.
Here, we proposed a new, bottom-up conceptual paradigm: The Energy Homeostasis Principle, where the balance between energy income, expenditure, and availability are the key parameters in determining the dynamics of neuronal phenomena found from molecular to behavioral levels. Neurons display high energy consumption relative to other cells, with metabolic consumption of the brain representing 20% of the whole-body oxygen uptake, which contrasts with this organ representing only 2% of the body weight. Also, neurons have specialized surrounding tissue that provides the necessary energy which in the case of the central nervous system, is provided by astrocytes. Moreover, and unlike other cell types with high energy demands such as muscle cells, neurons have a strict aerobic metabolism. These facts indicate that neurons are highly sensitive to energy limitations with Gibb’s free energy dictating the direction of all cellular metabolic processes. From this activity, the largest energy is by far, expended by action potentials and post-synaptic potentials, therefore, plasticity can be reinterpreted on their energy context. Consequently, neurons, through their synapses, would impose energy demands over post-synaptic neurons in a close loop-manner, modulating the dynamics of local circuits. Therefrom, energy dynamics end up impacting the homeostatic mechanisms of neuronal networks. Furthermore, local energy management emerges also as a neural population property, where most of the energy expenses are triggered by sensory or other modulatory input. Local energy management in neurons may be sufficient to explain the emergence of behavior, enabling the assessment of which and how properties arise in neural circuits. Importantly, the Energy Homeostasis Principle proposal is also readily testable in simple neuronal networks.

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