We are happy to announce the release of **Brian2GeNN 1.2**, a new release of the Brian 2 interface with the [GeNN simulator](http://genn-team.github.io/genn/). This release fixes compatibility issues with the latest versions of Brian and GeNN, and adds support for single precision floating point numbers. Using single precision instead of the default double precision can lead to significantly increased performance, especially on consumer-oriented GPUs.
**Note**: We no longer provide conda packages for Brian2GeNN, please install it via `pip` (`pip install brian2genn`), install GeNN and set `GENN_PATH`. Please see the [installation instructions](https://brian2genn.readthedocs.io/en/1.2/introduction/index.html#installing-the-brian2genn-interface) for details.
See below for a list of the most important changes.
New features
- Support for single precision floating point numbers. Switching to single precision is done by setting Brian's `core.default_float_dtype` preference to `float32`.
- Some GeNN features have been exposed to Brian2GeNN as preferences, e.g. the parallelization strategy for synapses can be set via the `devices.genn.synapse_span_type` preference, and the CUDA block size for the different kernels via preferences such as `devices.genn.neuron_blocksize`. For a full list of preferences, see [the documentation](https://brian2genn.readthedocs.io/en/1.2/introduction/preferences.html).
- More "code slots" are available to add custom C++ code to the simulation (see the [`insert_code` documentation](https://brian2genn.readthedocs.io/en/1.2/reference/brian2genn.device.GeNNDevice.html#brian2genn.device.GeNNDevice.insert_code) for details).
- GeNN's internal profiling routines can be activated via Brian2GeNN's `devices.genn.kernel_timing` preference.
Bug fixes
- `Network` objects are now supported (66). Thanks to Yuancheng Xu for reporting this error.
- Fixed outdated documentation (76, 75). Thanks to Justas Birgiolas for reporting these issues.
Infrastructure/packaging changes
- Packaging: Brian2GeNN is no longer packaged as a conda package, because the bundled pre-compiled GeNN libraries were not always working correctly. Please install Brian2GeNN from source via pip (`pip install brian2genn`) and follow [GeNN's installation instructions](http://genn-team.github.io/genn/documentation/3/html/Installation.html) to install GeNN. Old versions of the Brian2GeNN conda packages have been moved to the [`archive` label](https://anaconda.org/brian-team/brian2genn/files?channel=archive).
- Our testing infrastructure now uses [Microsoft Azure Pipelines](https://azure.microsoft.com/en-us/services/devops/pipelines/) instead of [travis](http://travis-ci.org/) and [appveyor](http://ci.appveyor.com/).
1.0-benchmark
This is the version that was used for creating benchmark runs in an upcoming paper on brian2genn. It does not yet contain the solution for the glibc bug and for the upcoming paper, the environment variable "LD_BIND_NOW" was set by hand before any benchmark runs were performed.
**This release is for reference only, please use the most recent release of the master branch for your research.**