A pervasive challenge in neuroscience is testing whether neuronal connectivity changes over time due to specific causes, such as stimuli, events, or clinical interventions. Recent hardware innovations and falling data storage costs enable longer, more naturalistic neuronal recordings. The implicit opportunity for understanding the self-organised brain calls for new analysis methods that link temporal scales: from the order of milliseconds over which neuronal dynamics evolve, to the order of minutes, days, or even years over which experimental observations unfold. This review article demonstrates how hierarchical generative models and Bayesian inference help to characterise neuronal activity across different time scales. Crucially, these methods go beyond describing statistical associations among observations and enable inference about underlying mechanisms. We offer an overview of fundamental concepts in state-space modeling and suggest a taxonomy for these methods. Additionally, we introduce key mathematical principles that underscore a separation of temporal scales, such as the slaving principle, and review Bayesian methods that are being used to test hypotheses about the brain with multiscale data. We hope that this review will serve as a useful primer for experimental and computational neuroscientists on the state of the art and current directions of travel in the complex systems modelling literature. Author Summary Exploring changes in brain connectivity over time is a major challenge in neuroscience. This review article discusses the application of hierarchical generative models and Bayesian statistical methods to investigate modulators of neuronal dynamics across different temporal scales. By utilizing these innovative techniques, researchers can move beyond describing statistical associations and ask questions about underlying mechanisms. The article provides an overview of state-space modelling, dynamics, and a taxonomy of methods. It also introduces mathematical principles that link temporal scales and reviews the use of Bayesian statistical methods in testing hypotheses about the brain using multiscale data. This review aims to serve as a resource for experimental and computational neuroscientists by presenting the current state of the art and future directions of travel in modelling literature.