updated release notes and fixed tenn typo in tutorials

doxygen/09_ReleaseNotes.dox

@@ -20,7 +20,7 @@ Bug fixes:
 ----
 1. Fixed support for CUDA 8 and older.
 2. Replaced deprecated ``__linux`` macro with ``__linux__`` making GeNN compatible with compiler on POWER9 Linux.
-
+3. Fixed bug where the initialisation of neuron groups which are identical apart from one needing an RNG could be incorrectly merged.
 
 Release Notes for GeNN v4.3.3 (PyGeNN 0.4.3)
 ====

doxygen/11_Tutorial.dox

@@ -186,9 +186,9 @@ int main()
 \section BuildingSimUnix Building the simulator (Linux or Mac)
 On Linux and Mac, GeNN simulations are typically built using a simple Makefile which can be generated with the following command:
 \code
->> genn-create-user-project.sh tennHHModel tenHHSimulation.cc
+>> genn-create-user-project.sh tenHHModel tenHHSimulation.cc
 \endcode
-This defines that the model is named tennHHModel and the simulation code is given in the file `tenHHSimulation.cc` that we completed above.
+This defines that the model is named tenHHModel and the simulation code is given in the file `tenHHSimulation.cc` that we completed above.
 Now type
 \code
 >> make
@@ -198,22 +198,22 @@ Now type
 So that projects can be easily debugged within the Visual Studio IDE (see section \ref Debugging for more details), Windows projects are built using an MSBuild script typically with the same title as the final executable.
 A suitable solution and project can be generated automatically with the following command:
 \code
->> genn-create-user-project.bat tennHHModel tenHHSimulation.cc
+>> genn-create-user-project.bat tenHHModel tenHHSimulation.cc
 \endcode
-his defines that the model is named tennHHModel and the simulation code is given in the file `tenHHSimulation.cc` that we completed above.
+his defines that the model is named tenHHModel and the simulation code is given in the file `tenHHSimulation.cc` that we completed above.
 Now type
 \code
->> msbuild tennHHModel.sln /p:Configuration=Release
+>> msbuild tenHHModel.sln /p:Configuration=Release
 \endcode
 
 \section RunningSim Running the Simulation
 You can now execute your newly-built simulator on Linux or Mac with
 \code
->> ./tennHHModel
+>> ./tenHHModel
 \endcode
 Or on Windows with
 \code
->> tennHHModel_Release
+>> tenHHModel_Release
 \endcode
 The output you obtain should look like
 \code

doxygen/12_Tutorial_Python.dox

@@ -18,7 +18,7 @@ from pygenn.genn_model import GeNNModel
 \endcode
 Two standard elements to the model definition are setting the simulation step size, the name of the model and the precision to simulate with:
 \code
-model = GeNNModel("float", "tennHH")
+model = GeNNModel("float", "tenHH")
 model.dT = 0.1
 \endcode
 \note 
@@ -64,7 +64,7 @@ This completes the model definition in this example. The complete `tenHH.py` fil
 \code
 from pygenn.genn_model import GeNNModel
 
-model = GeNNModel("float", "tennHH")
+model = GeNNModel("float", "tenHH")
 model.dT = 0.1
 
 p = {"gNa": 7.15,   # Na conductance in [muS]
@@ -115,7 +115,7 @@ This completes the first version of the script. The complete `tenHH.py` file sho
 \code
 from pygenn.genn_model import GeNNModel
 
-model = GeNNModel("float", "tennHH")
+model = GeNNModel("float", "tenHH")
 model.dT = 0.1
 
 p = {"gNa": 7.15,   # Na conductance in [muS]

doxygen/14_Tutorial_Python.dox

@@ -11,7 +11,7 @@ First, copy the files from Tutorial 1 into a new directory and rename the ``tenH
 \endcode
 Finally, to reduce confusion we should rename the model itself. Open `tenHHRing.py`, change the model name inside,
 \code
-model = GeNNModel("float", "tennHHRing")
+model = GeNNModel("float", "tenHHRing")
 \endcode
 You will also need to import some additional PyGeNN functions and constants:
 \code
@@ -21,7 +21,7 @@ from pygenn.genn_wrapper import NO_DELAY
 \endcode
 
 \section SynapseMatrix Defining the Detailed Synaptic Connections
-We want to connect our ten neurons into a ring where each neuron connects to its neighbours. In order to initialise this connectivity we need to add a sparse connectivity initialisation snippet at the top of ``tennHHRing.cc``:
+We want to connect our ten neurons into a ring where each neuron connects to its neighbours. In order to initialise this connectivity we need to add a sparse connectivity initialisation snippet at the top of ``tenHHRing.cc``:
 \code
 ring_model = create_custom_sparse_connect_init_snippet_class(
     "ring",
@@ -93,7 +93,7 @@ ring_model = create_custom_sparse_connect_init_snippet_class(
     calc_max_row_len_func=create_cmlf_class(
         lambda num_pre, num_post, pars: 1)())
 
-model = GeNNModel("float", "tennHHRing")
+model = GeNNModel("float", "tenHHRing")
 model.dT = 0.1
 
 p = {"gNa": 7.15,   # Na conductance in [muS]
@@ -183,7 +183,7 @@ ring_model = create_custom_sparse_connect_init_snippet_class(
     calc_max_row_len_func=create_cmlf_class(
         lambda num_pre, num_post, pars: 1)())
 
-model = GeNNModel("float", "tennHHRing")
+model = GeNNModel("float", "tenHHRing")
 model.dT = 0.1
 
 p = {"gNa": 7.15,   # Na conductance in [muS]