A central motivation for using ngc-learn is to flexibly build computational models of neuronal information processing, dynamics, and credit assignment (as well as design one's own custom instantiations of their mathematical formulations and ideas). In this set of tutorials, we will go through the central basics of using ngc-learn's in-built biophysical components, also called "cells" and "synapses", to craft and simulate adaptive neural systems.
Usefully, ngc-learn starts with a collection of cells -- those that are partitioned into those that are graded / real-valued (ngclearn.components.neurons.graded) and those that spike (ngclearn.components.neurons.spiking). In addition, ngc-learn supports another collection called synapses -- generally, those that are learned with Hebbian schemes (ngclearn.components.synapses.hebbian) such as spike-timing-dependent plasticity and multi-factor rules. With the in-built, standard cells and synapses in these two core collections, you can readily construct a wide variety of models, recovering many classical ones previously proposed in research in computational neuroscience and brain-inspired computing (many of these models are available for external download in the Model Museum.
While the reader is free to jump into any one self-contained tutorial in any order based on their needs, we organize, within each topic, the lessons starting from more basic, foundational modeling modules and library tools and sequentially work towards more advanced concepts.
input_cells traces
simple_leaky_integrator lif fitzhugh_nagumo_cell izhikevich_cell adex_cell
rate_cell error_cell
hebbian stdp
plotting metrics integration