nest · jasperalbers · Aug 4, 2022 · Aug 4, 2022 · Aug 4, 2022 · Aug 9, 2022
diff --git a/doc/htmldoc/examples/index.rst b/doc/htmldoc/examples/index.rst
@@ -42,18 +42,6 @@ PyNEST examples
 
 .. grid:: 1 1 2 3
 
-    .. grid-item-card:: NEST Sudoku solver
-           :img-top: ../static/img/sudoku_solution.gif
-
-           * :doc:`../auto_examples/sudoku/sudoku_solver`
-           * :doc:`../auto_examples/sudoku/plot_progress`
-
-    .. grid-item-card:: NEST Pong game
-           :img-top: ../static/img/pong_sim.gif
-
-           * :doc:`../auto_examples/pong/run_simulations`
-           * :doc:`../auto_examples/pong/generate_gif`
-
     .. grid-item-card:: Astrocytes
            :img-top: ../static/img/astrocyte_interaction.png
 
@@ -63,6 +51,18 @@ PyNEST examples
            * :doc:`../auto_examples/astrocytes/astrocyte_brunel_bernoulli`
            * :doc:`../auto_examples/astrocytes/astrocyte_brunel_fixed_indegree`
 
+    .. grid-item-card::  Discrete state neurons
+           :img-top: ../static/img/nest_logo-faded.png
+
+           * :doc:`Susceptible (S), Infected (I), Recovered (R) model of the spread of disease <../auto_examples/sir_neuron>`
+
+    .. grid-item-card:: Rate neurons
+           :img-top: ../static/img/pynest/rate_neuron.png
+
+           * :doc:`../auto_examples/lin_rate_ipn_network`
+           * :doc:`../auto_examples/rate_neuron_dm`
+
+
 
 .. grid:: 1 1 2 3
 
@@ -106,7 +106,6 @@ PyNEST examples
            * :doc:`../auto_examples/compartmental_model/two_comps`
 
 
-
     .. grid-item-card:: GIF (from Gerstner lab)
            :img-top: ../static/img/pynest/gif_pop.png
 
@@ -117,12 +116,6 @@ PyNEST examples
 
 .. grid:: 1 1 2 3
 
-    .. grid-item-card:: Rate neurons
-           :img-top: ../static/img/pynest/rate_neuron.png
-
-           * :doc:`../auto_examples/lin_rate_ipn_network`
-           * :doc:`../auto_examples/rate_neuron_dm`
-
 
     .. grid-item-card:: Hodgkin-Huxley
            :img-top: ../static/img/pynest/hh_phase.png
@@ -135,15 +128,16 @@ PyNEST examples
 
            * :doc:`../auto_examples/BrodyHopfield`
 
-.. grid:: 1 1 2 3
-
     .. grid-item-card:: Brette and Gerstner
            :img-top: ../static/img/pynest/brette_gerstner2c.png
 
            * :doc:`../auto_examples/brette_gerstner_fig_2c`
            * :doc:`../auto_examples/brette_gerstner_fig_3d`
 
 
+
+.. grid:: 1 1 2 3
+
     .. grid-item-card:: Precise spiking
            :img-top: ../static/img/pynest/precisespiking.png
 
@@ -154,13 +148,27 @@ PyNEST examples
 
            * :doc:`../auto_examples/CampbellSiegert`
 
-.. grid:: 1 1 2 3
-
-      .. grid-item-card:: Decision making in recurrent network (after Wang 2002)
+    .. grid-item-card:: Decision making in recurrent network (after Wang 2002)
            :img-top: ../static/img/nest_logo-faded.png
 
            * :doc:`../auto_examples/wang_decision_making`
 
+
+.. grid:: 1 1 2 3
+
+    .. grid-item-card:: NEST Sudoku solver
+           :img-top: ../static/img/sudoku_solution.gif
+
+           * :doc:`../auto_examples/sudoku/sudoku_solver`
+           * :doc:`../auto_examples/sudoku/plot_progress`
+
+    .. grid-item-card:: NEST Pong game
+           :img-top: ../static/img/pong_sim.gif
+
+           * :doc:`../auto_examples/pong/run_simulations`
+           * :doc:`../auto_examples/pong/generate_gif`
+
+
 .. grid:: 1 1 2 3
 
     .. grid-item-card:: Gap junctions

diff --git a/doc/htmldoc/neurons/neuron_types.rst b/doc/htmldoc/neurons/neuron_types.rst
@@ -496,7 +496,7 @@ The following non-linearity types are available:
   *  :doc:`/models/tanh_rate`
   *  :doc:`/models/threshold_lin_rate`
 
-Where is Non-linearity applied? 
+Where is Non-linearity applied?
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 
@@ -627,11 +627,12 @@ Multi-state neurons
 
      Neurons with two or three discrete states. These are the simplest neuron models with threshold activation. Binary neurons have
      On / Off behavior used in theoretical neuroscience and disease theory.
+     The SIR neurons implement 3 discrete states based on a mathematical theory of epidemics: susceptible (S), infected (I), and recovered (R).
 
 .. dropdown:: Multi-state neurons
 
     {% for items in tag_dict %}
-    {% if items.tag == "binary" %}
+    {% if items.tag == "discrete state" %}
     {% for item in items.models | sort %}
     * :doc:`/models/{{ item | replace(".html", "") }}`
     {% endfor %}

diff --git a/libnestutil/nest_types.h b/libnestutil/nest_types.h
@@ -183,8 +183,10 @@ enum SignalType
   NONE = 0,
   SPIKE = 1,
   BINARY = 2,
-  ALL = SPIKE | BINARY
+  SIR = 3,
+  SIRS = 4,
+  SIS = 5,
+  ALL = SPIKE | BINARY | SIR | SIRS | SIS
 };
 }
-
 #endif /* #ifndef NEST_TYPES_H */
diff --git a/models/erfc_neuron.h b/models/erfc_neuron.h
@@ -29,7 +29,7 @@
 namespace nest
 {
 
-/* BeginUserDocs: neuron, binary
+/* BeginUserDocs: neuron, discrete state
 
 Short description
 +++++++++++++++++

diff --git a/models/ginzburg_neuron.h b/models/ginzburg_neuron.h
@@ -29,7 +29,7 @@
 namespace nest
 {
 
-/* BeginUserDocs: neuron, binary
+/* BeginUserDocs: neuron, discrete state
 
 Short description
 +++++++++++++++++

diff --git a/models/mcculloch_pitts_neuron.h b/models/mcculloch_pitts_neuron.h
@@ -29,7 +29,7 @@
 namespace nest
 {
 
-/* BeginUserDocs: neuron, binary
+/* BeginUserDocs: neuron, discrete state
 
 Short description
 +++++++++++++++++