correct and clarify code generation for plastic synapses in tutorial notebooks (#1235)

Author: clinssen

doc/tutorials/stdp_dopa_synapse/stdp_dopa_synapse.ipynb

@@ -121,11 +121,9 @@
     "\n",
     "In this tutorial, we will use a very simple integrate-and-fire model, where arriving spikes cause an instantaneous increment of the membrane potential, the \"iaf_psc_delta\" model.\n",
     "\n",
-    "We first define a helper function to generate the C++ code for the models, build it as a NEST extension module, and load the module into the kernel. The resulting model names are composed of associated neuron and synapse partners, because of the co-generation, for example, \"stdp_synapse__with_iaf_psc_delta\" and \"iaf_psc_delta__with_stdp_synapse\".\n",
+    "When NESTML is invoked, the C++ code is generated for the models, and then built (compiled) as a NEST extension module, which is then loaded into the NEST kernel at runtime using ``nest.Install()``.\n",
     "\n",
-    "Because NEST does not support un- or reloading of modules at the time of writing, we implement a workaround that appends a unique number to the name of each generated model, for example, \"stdp_synapse_3cc945f__with_iaf_psc_delta_3cc945f\" and \"iaf_psc_delta_3cc945f__with_stdp_synapse_3cc945f\".\n",
-    "\n",
-    "The resulting neuron and synapse model names are returned by the function, so we do not have to think about these internals."
+    "The resulting neuron and synapse model names are returned by the function, because when generating code for plastic synapses, there is typically a tight integration of the generated with code with that of the postsynaptic neuron model. For more information about this, please see the NESTML documentation section [Generating code for plastic synapses](https://nestml.readthedocs.io/en/latest/running/running_nest.html#generating-code-for-plastic-synapses). Hence, the resulting model names are composed of associated neuron and synapse partners, because of the co-generation, for example, ``\"stdp_synapse__with_iaf_psc_delta\"`` and ``\"iaf_psc_delta__with_stdp_synapse\"``."
    ]
   },
   {

doc/tutorials/stdp_third_factor_active_dendrite/stdp_third_factor_active_dendrite.ipynb

@@ -471,7 +471,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We will use a helper function, ``generate_code_for()``, to generate the C++ code for the models and build it as a NEST extension module. Afterwards we will be able to dynamically load the module into the kernel using ``nest.Install()``."
+    "When NESTML is invoked, the C++ code is generated for the models, and then built (compiled) as a NEST extension module, which is then loaded into the NEST kernel at runtime using ``nest.Install()``.\n",
+    "\n",
+    "The resulting neuron and synapse model names are returned by the function, because when generating code for plastic synapses, there is typically a tight integration of the generated with code with that of the postsynaptic neuron model. For more information about this, please see the NESTML documentation section [Generating code for plastic synapses](https://nestml.readthedocs.io/en/latest/running/running_nest.html#generating-code-for-plastic-synapses). Hence, the resulting model names are composed of associated neuron and synapse partners, because of the co-generation, for example, ``\"third_factor_stdp_synapse__with_iaf_psc_exp_active_dendrite_neuron\"`` and ``\"iaf_psc_exp_active_dendrite_neuron__with_third_factor_stdp_synapse\"``."
    ]
   },
   {

doc/tutorials/stdp_windows/stdp_windows.ipynb

@@ -237,7 +237,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Generate the code, build the user module and make the model available to instantiate in NEST:"
+    "When NESTML is invoked, the C++ code is generated for the models, and then built (compiled) as a NEST extension module, which is then loaded into the NEST kernel at runtime using ``nest.Install()``.\n",
+    "\n",
+    "The resulting neuron and synapse model names are returned by the function, because when generating code for plastic synapses, there is typically a tight integration of the generated with code with that of the postsynaptic neuron model. For more information about this, please see the NESTML documentation section [Generating code for plastic synapses](https://nestml.readthedocs.io/en/latest/running/running_nest.html#generating-code-for-plastic-synapses). Hence, the resulting model names are composed of associated neuron and synapse partners, because of the co-generation, for example, ``\"stdp_synapse__with_iaf_psc_delta\"`` and ``\"iaf_psc_delta__with_stdp_synapse\"``."
    ]
   },
   {

doc/tutorials/triplet_stdp_synapse/triplet_stdp_synapse.ipynb

@@ -240,6 +240,16 @@
     "\"\"\""
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "385b96ca",
+   "metadata": {},
+   "source": [
+    "When NESTML is invoked, the C++ code is generated for the models, and then built (compiled) as a NEST extension module, which is then loaded into the NEST kernel at runtime using ``nest.Install()``.\n",
+    "\n",
+    "The resulting neuron and synapse model names are returned by the function, because when generating code for plastic synapses, there is typically a tight integration of the generated with code with that of the postsynaptic neuron model. For more information about this, please see the NESTML documentation section [Generating code for plastic synapses](https://nestml.readthedocs.io/en/latest/running/running_nest.html#generating-code-for-plastic-synapses). Hence, the resulting model names are composed of associated neuron and synapse partners, because of the co-generation, for example, ``\"stdp_triplet_synapse__with_iaf_psc_delta\"`` and ``\"iaf_psc_delta__with_stdp_triplet_synapse\"``."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 3,