Add detailed neuron documentation

doc/htmldoc/_ext/model_tag_setup.py

@@ -356,7 +356,7 @@ def template_renderer(app, docname, source):
         and used to inject the rendered content.
     """
     env = app.builder.env
-    template_files = ["models/index", "neurons/index", "synapses/index", "devices/index"]
+    template_files = ["models/index", "neurons/index", "synapses/index", "devices/index", "neurons/neuron_types"]
 
     # Render the document if it matches one of the specified templates
     if any(docname == template_file for template_file in template_files):

doc/htmldoc/get-started_index.rst

@@ -147,6 +147,8 @@ More topics
        |neuron| More about neurons
        ^^^
 
+       * :ref:`types_neurons`
+       * :ref:`neuron_update`
        * :ref:`sim_precise_spike_times`
        * :ref:`exact_integration`
 
@@ -211,6 +213,8 @@ More topics
 
    tutorials/index
    tutorials/pynest_tutorial/index
+   neurons/neuron_update
+   neurons/neuron_types
 
 .. toctree::
    :maxdepth: 1

doc/htmldoc/index.rst

@@ -100,7 +100,7 @@ How NEST works --- The Big Picture
 
 
         A NEST simulation is created with input from :doc:`stimulation devices </devices/index>`,
-        :doc:`neuron models </neurons/index>`, and :doc:`synapse models </synapses/index>`,
+        :doc:`neuron models </neurons/index>`, and :doc:`synapstic plasticity models </synapses/index>`,
         along with  :ref:`connection rules <connectivity_concepts>`.
         You can choose what data to record with :doc:`recording devices </devices/index>`.
         After simulation, the output is ready for analysis with NEST's built in :py:mod:`.raster_plot` and :py:mod:`.voltage_trace`

doc/htmldoc/models/index.rst

@@ -93,7 +93,6 @@ Learn more about . . .
       * :ref:`Model terminology <model_meaning>`
 
 
-
 ----
 
 .. _model_meaning:
@@ -115,10 +114,7 @@ The term `models` in the context of NEST (and the field of computational neurosc
 2. **Network models**. These models are created from individual neuron
    and synapse models using the different commands provided by the
    :ref:`PyNEST API <pynest_api>`. Examples for such network models
-   are the :doc:`microcircuit model
-   <../auto_examples/Potjans_2014/index>` or the `multi-area model
-   <https://inm-6.github.io/multi-area-model/>`_).
-   You can also explore :doc:`all our example networks </examples/index>`.
+    can be found :doc:`here </examples/index>`.
 
 .. seealso::
 

doc/htmldoc/nest_behavior/running_simulations.rst

@@ -38,6 +38,7 @@ The simulation loop. Light gray boxes denote thread parallel parts, dark
 gray boxes denote :hxt_ref:`MPI` parallel parts. U(St) is the update operator that
 propagates the internal state of a neuron or device.
 
+
 .. _simulation_resolution:
 
 Simulation resolution and update interval

doc/htmldoc/neurons/index.rst

@@ -4,72 +4,87 @@ All about neurons in NEST
 =========================
 
 
-Guides on using neurons in NEST
--------------------------------
-
-.. grid:: 1 1 2 2
+.. grid:: 1 1 3 3
    :gutter: 1
 
-   .. grid-item::
+   .. grid-item-card::
+       :class-header: sd-d-flex-row sd-align-minor-center sd-bg-info sd-text-white
 
-     .. grid:: 1 1 1 1
 
-        .. grid-item-card:: Node management (neurons and devices)
+       |neuron|  Neuron types in NEST
+       ^^^
 
-             * :ref:`node_handles`
-             * :ref:`param_ex`
 
-   .. grid-item::
+       * :ref:`types_neurons`: Find out about the various neuron models and their mechanisms that are available in NEST
 
-     .. grid:: 1 1 1 1
+       * :ref:`neuron_update`: Learn how NEST handles the steps to update the dynamics of neurons during simulation.
 
-        .. grid-item-card:: Exact integration
-           :class-title: sd-d-flex-row sd-align-minor-center
-           :link: exact_integration
-           :link-type: ref
 
-        .. grid-item-card:: Precise spike times
-           :class-title: sd-d-flex-row sd-align-minor-center
-           :link: sim_precise_spike_times
-           :link-type: ref
+   .. grid-item-card::
+       :class-header: sd-d-flex-row sd-align-minor-center sd-bg-info sd-text-white
 
-.. toctree::
-  :maxdepth: 1
-  :glob:
-  :hidden:
 
-  *
+       |python| Using neurons in PyNEST scripts
+       ^^^
+
+       * :ref:`Manipulating nodes (neurons and devices) <node_handles>`: understand basic functionality of nodes
+       * :ref:`param_ex`: explore how to use parameter objects in NEST
+
 
-.. dropdown:: List of neuron models
-   :color: info
+   .. grid-item-card::
+       :class-header: sd-d-flex-row sd-align-minor-center sd-bg-info sd-text-white
 
-   {% for items in tag_dict %}
-   {% if items.tag == "neuron" %}
-   {% for item in items.models | sort %}
-   * :doc:`/models/{{ item | replace(".html", "") }}`
-   {% endfor %}
-   {% endif %}
-   {% endfor %}
 
+       |math| Find a specific model
+       ^^^
 
-Neuron model naming conventions
--------------------------------
 
-Neuron model names in NEST combine abbreviations that describe the dynamics and synapse specifications for that model.
-They may also include the author's name of a model based on a specific paper.
+       For details on individual models, please take alook at our model directory, where you can
+       select various tags and refine the results to your choosing, or look up our A-Z list.
 
-For example, the neuron model name
+       * :doc:`/models/index`
 
-``iaf_cond_beta``
 
-    corresponds to an implementation of a spiking neuron using
-    integrate-and-fire dynamics with conductance-based
-    synapses. Incoming spike events induce a postsynaptic change of
-    conductance modeled by a beta function.
 
-As an example for a neuron model name based on specific paper,
+----
 
-``hh_cond_exp_traub``
+.. |nav| image:: /static/img/GPS-Settings-256_nest.svg
+.. |script| image:: /static/img/script_white.svg
+      :scale: 20%
+.. |start| image:: /static/img/start_white.svg
+      :scale: 40%
+.. |user| image:: /static/img/020-user.svg
+.. |teacher| image:: /static/img/014-teacher.svg
+.. |admin| image:: /static/img/001-shuttle.svg
+.. |dev| image:: /static/img/dev_orange.svg
+.. |nestml| image:: /static/img/nestml-logo.png
+      :scale: 15%
+.. |synapse| image:: /static/img/synapse_white.svg
+.. |neuron|  image:: /static/img/neuron_white.svg
+.. |glossary|  image:: /static/img/glossary_white.svg
+.. |git|  image:: /static/img/git_white.svg
+.. |refresh|  image:: /static/img/refresh_white.svg
+.. |hpc|  image:: /static/img/hpc_white.svg
+.. |random|  image:: /static/img/random_white.svg
+.. |math|  image:: /static/img/math_white.svg
+.. |network|  image:: /static/img/network_brain_white.svg
+.. |device|  image:: /static/img/device_white.svg
+.. |connect|  image:: /static/img/connect_white.svg
+.. |sonata|  image:: /static/img/sonata_white.svg
+.. |write|  image:: /static/img/write_nest_white.svg
+      :scale: 60%
+.. |parallel| image:: /static/img/parallel_white.svg
+.. |simulate| image:: /static/img/simulate_white.svg
+.. |interactive| image:: /static/img/interactive_white.svg
+.. |python| image:: /static/img/python_white.svg
+.. |gallery| image:: /static/img/gallery_white.svg
+
+
+.. toctree::
+   :hidden:
 
-    implements a modified version of the Hodgkin Huxley neuron model
-    based on Traub and Miles (1991)
+   neuron_types
+   parametrization
+   node_handles
+   exact-integration
+   simulations_with_precise_spike_times