diff --git a/doc/running/index.rst b/doc/running/index.rst
index 9d5c50305..055a3aec3 100644
--- a/doc/running/index.rst
+++ b/doc/running/index.rst
@@ -245,17 +245,30 @@ This will generate, compile, build, and install the code for a set of specified
    * - ``--codegen_opts``
      - (Optional) Path to a JSON file containing additional options for the target platform code generator. A list of available options can be found under the section "Code generation options" for your intended target platform on the page :ref:`Running NESTML`.
 
-NEST Desktop target
-~~~~~~~~~~~~~~~~~~~
+Filesystem permissions
+~~~~~~~~~~~~~~~~~~~~~~
 
-The aim of the NEST Desktop as target is to generate ``json`` files for the neuron models. The resulting file contains details about the state variables, parameters and their initial values defined in their respective ``.nestml`` files. The ``json`` files are used to load them in the NEST Desktop user interface.
-
-For example, for the neuron model ``iaf_psc_exp``, the ``json`` file will be generated by running the ``generate_target`` function with ``target_platform`` option set to ``NEST_DESKTOP``.
+If you are using NESTML on a shared server or high performance computing (HPC) system, it could be that NESTML was installed in a location in the filesystem to which you do not have write access. In this case, make sure to explicitly set the ``install_path`` when generating the target code to a directory to which you have write access, for example, inside your home directory. For example:
 
 .. code-block:: python
 
-   from pynestml.frontend.pynestml_frontend import generate_target
+   from pynestml.frontend.pynestml_frontend import generate_nest_target
 
-   generate_target(input_path="/home/nest/work/pynestml/models/neurons/iaf_psc_exp.nestml",
-                   target_platform="NEST_DESKTOP",
-                   target_path="/tmp/nestml_target")
+   nestml_file_path = "~/nestml-neurons"    # this should be a path inside your home directory
+                                            # or other directory to which you have write permission
+   nestml_target_path = nestml_file_path + "/nestml_target/"
+   nestml_install_path = nestml_file_path + "/nestml_install/"
+
+   print(f"Generating NEST target code from {nestml_file_path} to {nestml_target_path}")
+
+   generate_nest_target(
+        input_path=str(nestml_file_path),
+        target_path=str(nestml_target_path),
+        install_path=str(nestml_install_path)
+   )
+
+Then, when running the Python simulation script which contains the ``nest.Install(<module_name>)``, make sure to first set the ``LD_LIBRARY_PATH``, such as:
+
+.. code-block:: bash
+
+   export LD_LIBRARY_PATH="~/nestml-neurons/nestml_install":$LD_LIBRARY_PATH
diff --git a/doc/running/running_nest_desktop.rst b/doc/running/running_nest_desktop.rst
index 5b1ce6c30..546d1db62 100644
--- a/doc/running/running_nest_desktop.rst
+++ b/doc/running/running_nest_desktop.rst
@@ -3,7 +3,17 @@ NEST Desktop target
 
 *NESTML features supported:* :doc:`neurons </nestml_language/neurons_in_nestml>`
 
-The aim of the NEST Desktop target is to generate ``json`` files with parameters for models. This data will be used for rendering the parameter values in the NEST Desktop UI when a NESTML model is loaded.
+The aim of the NEST Desktop as target is to generate ``json`` files for the neuron models. The resulting file contains details about the state variables, parameters and their initial values defined in their respective ``.nestml`` files. The ``json`` files are used to load them in the NEST Desktop user interface.
+
+For example, for the neuron model ``iaf_psc_exp``, the ``json`` file will be generated by running the ``generate_target`` function with ``target_platform`` option set to ``NEST_DESKTOP``.
+
+.. code-block:: python
+
+   from pynestml.frontend.pynestml_frontend import generate_target
+
+   generate_target(input_path="/home/nest/work/pynestml/models/neurons/iaf_psc_exp.nestml",
+                   target_platform="NEST_DESKTOP",
+                   target_path="/tmp/nestml_target")
 
 
 Code generation options