# Introduction NGC-Learn is a general-purpose library for modeling complex dynamical systems, particularly those that are useful for computational neuroscience, neuroscience-motivated artificial intelligence (NeuroAI), and brain-inspired computing. While the library is designed to provide flexibility on the experimenter/designer side -- allowing one to develop their own dynamics and evolutionary processes -- at its foundation are a few standard components. These are basic modeling nodes for simulating some common biophysical systems computationally, which are useful to know when getting started and for quickly building some classical/historical models. If you are interested in knowing some of the neurophysiological theory behind NGC-Learn's design philosophy, [this section](../tutorials/theory) might be of interest. Specifically, to make best use of NGC-Learn, it is important to get the hang of its "nodes-and-cables system" (the historical name for its backend engine) in order to build simulation objects. This set of tutorials will walk you through, step-by-step, the key aspects of the library that you will need to know so that you can build and run simulations of computational biophysical models. In addition, we provide walkthroughs of some of the central mechanisms underlying NGC-Sim-Lib, the simulation dependency library that drives NGC-Learn; these lessons are particularly useful for not only understanding why and how things are done by NGC-Learn's simulation backend engine but also for those who want to design new, custom extensions of NGC-Learn either for their own research or to help contribute to the development of the main library. ## Organization of Tutorials The core tutorials and usage lessons for using NGC-Learn can be found [here, in the modeling basics table of contents](../tutorials/index.rst) which essentially go through: the basic configuration and use of NGC-Learn (and NGC-Sim-Lib) to construct simulations of basic dynamical systems. More advanced tutorials related to the essentials of neurocognitive modeling -- such as building and analyzing neuroscience models of neuronal dynamics and synaptic plasticity -- can be found [here, in the neurocognitive modeling table of contents](../tutorials/neurocog/index.rst).