Introduction

ngc-learn is a general-purpose library for modeling biomimetic/neuro-mimetic complex systems. While the library is designed to provide flexibility on the experimenter/designer side – allowing one to design their own dynamics and evolutionary processes – at its foundation are a few standard components, the basic modeling nodes for simulating some common biophysical systems computationally, that useful to know in getting started and 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 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” (as it was historically referred to) in order to build simulation objects. This set of tutorials will walk through, step-by-step, the key aspects of the library you need to know so you can build and run simulations of computational biophysical models. In addition, we provide walkthroughs of some of the central mechanisms underlying ngcsimlib, the simulation dependency library that drives ngc-learn; these are particularly useful for not only understanding why and how things are done by ngc-learn’s simulation backend but also for those who want to design new, custom extensions of ngc-learn either for their own research or to contribute to the development of the main library.

Organization of Tutorials

The core tutorials and lessons for using ngc-learn can be found here, in the tutorial table of contents and go through: the basic configuration and use of ngc-learn and ngc-sim-lib to construct simulations of dynamical systems, the essentials of neurocognitive modeling (such as building and analyzing neuronal dynamics and synaptic plasticity), as well as the coverage of some key foundational ideas/tools worth knowing about ngc-learn (and its backend, ngc-sim-lib) particularly to facilitate easier debugging, experimental configuration, and advanced model tools like bundle rules.