Modern neuroscience understands the brain as a complex system whose functional properties are determined by the myriads of interactions of its cellular components. In the difficult route to obtain better understanding of this organ, mathematical tools have become ever more effective, and we are now able to simulate the dynamics of brain signals from digital representations that have high fidelity to experimental data. However, these simulations are generated by inherently simple neuronal models that contain assumptions that, by necessity, can be at times far removed from the underlying neurobiology. Here we develop an integrated view of brain dynamics that describes how the neurovascular units — made of neurons, capillaries and glia — collectively determine the development of the functional properties of brain neuronal populations and maintain their homeostatic control. These interactions are instructed by genetic programmes modulated by local environmental conditions. These complexities can be integrated into generative models and supply helpful analytics able to link the observable data to neurobiological entities if detailed brain mappings of molecular and cellular components are available. We further highlight how the development of these generative models could be relevant to the understanding of psychiatric conditions.