Merge pull request #501 from genn-team/4_7_release

4.7.0 release

doxygen/09_ReleaseNotes.dox

@@ -1,5 +1,24 @@
 /*! \page ReleaseNotes Release Notes
 
+Release Notes for GeNN v4.7.0
+====
+This release adds a number of significant new features to GeNN as well as including a number of bug fixes that have been identified since the 4.6.0 release.
+
+User Side Changes
+----
+1. While a wide range of convolutional type connectivity can be implemented using SynapseMatrixConnectivity::PROCEDURAL, the performance is often worse than sparse connectivity. SynapseMatrixConnectivity::TOEPLITZ provides a more efficient solution with InitToeplitzConnectivitySnippet::Conv2D and InitToeplitzConnectivitySnippet::AvgPoolConv2D implementing some typical connectivity patterns (see \ref sectToeplitzConnectivityInitialisation)
+2. Shared weight kernels had to be previously provided as extra global parameters via the InitVarSnippet::Kernel variable initialisation snippet. This meant kernels had to be manually allocated to the correct size and couldn't be initialised using standard functionality. SynapseMatrixWeight::KERNEL allows kernels to be treated as standard state variables (see \ref subsect34).
+3. Some presynaptic updates need to update the state of presynaptic neurons as well as postsynaptic. These updates can now be made using the \$(addToPre,...) function from presynaptic update code and the destination additional input variable can be specified using SynapseGroup::setPreTargetVar (see \ref sect34)
+4. On Windows, all models in the same directory would build their generated code into DLLs with the same name, prevented the the caching system introduced in v4.5.0 working properly. CodeGenerator::PreferencesBase::includeModelNameInDLL includes the name of the model in the DLL filename, resolving this problem. This is now the default behaviour in PyGeNN but, when using GeNN from C++, the flag must be manually set and MSBuild projects updated to link to the correct DLL.
+5. Neuron code can now sample the binomial distribution using \$(gennrand_binomial) and this can be used to initialise variables with InitVarSnippet::Binomial (see \ref neuron_rng and \ref sectVariableInitialisation)
+6. In the latest version of Windows Subsystem for Linux, CUDA is supported but libcuda is mounted in a non-standard location. GeNN's CUDA backend now adds this location to the linker paths.
+
+Bug fixes:
+----
+1. Fixed issues with some configurations of InitSparseConnectivitySnippet::Conv2D when stride > 1 which caused incorrect connectivity to be instantiated as well as crashes when this snippet was used to generate sparse connectivity.
+2. Fixed issue where, if \$(addToInSynDelay) was used in spike-like event code, it was not detected and dendritic delay structures were not correctly created.
+3. Fixed issue where precision wasn't being correctly applied to neuron additional input variable and  sparse connectivity row build state variable initialisation meaning double precision code could unintentially be generated.
+
 Release Notes for GeNN v4.6.0
 ====
 This release adds a number of significant new features to GeNN as well as several usability improvements for PyGeNN.