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Neurocognitive Modeling Lessons
===============================

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 custom instantiations of one's own 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 and biophysical
computational models.

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 adapt (or "learn") with
biological credit assignment building blocks (such as those in `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 computational neuroscience and brain-inspired computing research (many of these kinds of models are
available for external download in the `Model Museum <https://github.com/NACLab/ngc-museum>`_).

.. toctree::
  :maxdepth: 1
  :caption: Model and Analysis Tools

  plotting
  metrics
  integration
  density_modeling

.. toctree::
  :maxdepth: 1
  :caption: Sensory Input Encoding & Tracing

  input_cells
  traces

.. toctree::
  :maxdepth: 1
  :caption: Spiking Neuronal Cells

  simple_leaky_integrator
  lif
  fitzhugh_nagumo_cell
  izhikevich_cell
  adex_cell
  hodgkin_huxley_cell

.. toctree::
  :maxdepth: 1
  :caption: Graded Neuronal Cells

  rate_cell
  error_cell

.. toctree::
  :maxdepth: 1
  :caption: Synapses and Forms of Plasticity

  dynamic_synapses
  hebbian
  stdp
  mod_stdp
  short_term_plasticity