GeNN 4.4.0

Release Notes for GeNN v4.4.0 (PyGeNN 0.4.4)

This release adds a number of significant new features to GeNN and expands the documentation to cover using GeNN from Python with PyGeNN. It also includes a number of bug fixes that have been identified since the 4.3.3 release.

User Side Changes

1. Improved documentation covering PyGeNN now available from https://genn-team.github.io/genn/
2. New system for efficiently recording spikes from multiple timesteps into GPU memory (#372).
3. Connectivity can now be initialised using column-wise as well as row-wise sparse connectivity initialisation snippets (#374).
4. Support for 'kernel-based' connectivity, allowing efficient support for connectivity such as convolutions (#373).
5. Improved access to spike times from weight update models - previous spike times can now be accessed via $(prev_sT_pre) and $(prev_sT_post) (#376).
6. Added support for accessing spike-like-event times from weight update models via $(seT_pre) and $(prev_seT_pre) variables (#380)
7. Added support for continuous as well as spike-driven dynamics for pre and postsynaptic weight update model variables (#377).
8. Added experimental OpenCL backend - there are still issues outstanding but any feedback would be much appreciated (#340).
9. Improved supression of irrelevant NVCC warnings when optimizing block sizes (#361).
10. Added support for SM 8.0 and 8.6 architectures to CUDA backend (#361).
11. Number of presynaptic and postsynaptic neurons are now available via $(num_pre) and $(num_post) in all connectivity initialisation and procedural connectivity kernels (#384)

Bug fixes:

1. Fixed support for CUDA 8 and older (#361).
2. Replaced deprecated __linux macro with __linux__ making GeNN compatible with compiler on POWER9 Linux (#363).
3. Fixed bug where the initialisation of neuron groups which are identical apart from one needing an RNG could be incorrectly merged (#387).