![]() Or, in other words, one of the best ways to understand the functioning of the brain is trying to build one (Einevoll et al., 2019 Eliasmith & Trujillo, 2014). By way of conclusion, we present several hybrid generative models from recent literature in scientific machine learning, which can be efficiently deployed to yield interpretable models of neural dynamics. We discuss the limitations and unique dynamical traits of brain data and the complementary need for hypothesis- and data-driven modeling. While not all of these models span the intersection of neuroscience, AI, and system dynamics, all of them do or can work in tandem as generative models, which, as we argue, provide superior properties for the analysis of neuroscientific data. The studies covered in this paper include fundamental models in computational neuroscience, nonlinear dynamics, data-driven methods, as well as emergent practices. This review article gives a high-level overview of the approaches across different scales of organization and levels of abstraction. Recent developments in artificial intelligence (AI) have accelerated this progress. ![]() The principled design and discovery of biologically- and physically-informed models of neuronal dynamics has been advancing since the mid-twentieth century.
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