genn-team/genn: GeNN 4.2.0

Release Notes for GeNN v4.2.0 (PyGeNN 0.3) This release adds a number of new features to GeNN and its Python interface as well as fixing a number of bugs that have been identified since the 4.1.0 release. User Side Changes Kernel timings can now be enabled from python with pygenn.genn_model.GeNNModel.timing_enabled and subsequently accessed with pygenn.genn_model.GeNNModel.neuron_update_time, pygenn.genn_model.GeNNModel.init_time, pygenn.genn_model.GeNNModel.presynaptic_update_time, pygenn.genn_model.GeNNModel.postsynaptic_update_time, pygenn.genn_model.GeNNModel.synapse_dynamics_time and pygenn.genn_model.GeNNModel.init_sparse_time. Backends now generate getFreeDeviceMemBytes() function to allow free device memory to be queried from user simulation code. This is also exposed to Python via GeNNModel.free_device_mem_bytes property. GeNN preferences are now fully exposed to PyGeNN by passing kwargs to pygenn.genn_model.GeNNModel.__init__. Logging level can now be seperately specified for GeNN, the code generator, the SpineML generator and the backend and is accessible from PyGeNN. CodeGenerator::PreferencesBase::enableBitmaskOptimisations flag enables an alternative algorithm for updating synaptic matrices implemented with SynapseMatrixConnectivity::BITMASK which performs better on smaller GPUs and CPUs. If you are manually initialising matrices this adds padding to align words to rows of the matrix. SynapseMatrixConnectivity::PROCEDURAL and SynapseMatrixWeight::PROCEDURAL allow connectivity and synaptic weights to be generated on the fly rather than stored in memory. CodeGenerator::PreferencesBase::automaticCopy flag allows models to be built without the need for explicitly copying data between host and device. For CUDA backend this uses unified memory (https://devblogs.nvidia.com/unified-memory-cuda-beginners/). Speed of code compilation can be improved by building using multiple threads. This is now done everywhere where make or MSBuild is invocated automatically. Bug fixes: Fixed several bugs in extra global parameter implementation in PyGeNN. Floating point min and max should be calculated with fmax and fmin in code snippets - fixed in several models and user projects. Fixed issues with version of numpy required in PyGeNN (previously held back by an issue with PyNN