# ANNarchy
[](https://zenodo.org/badge/latestdoi/57382690)
ANNarchy (Artificial Neural Networks architect) is a parallel and hybrid simulator for distributed rate-coded or spiking neural networks. The core of the library is written in C++ and distributed using openMP or CUDA. It provides an interface in Python for the definition of the networks. It is released under the [GNU GPL v2 or later](http://www.gnu.org/licenses/gpl.html).
* Source code: [github.com/ANNarchy/ANNarchy](https://github.com/ANNarchy/ANNarchy)
* Documentation: [annarchy.github.io](https://annarchy.github.io)
* Forum: [google forum](https://groups.google.com/forum/#!forum/annarchy)
* Bug reports and feature requests: [Issue Tracker](https://github.com/ANNarchy/ANNarchy/issues).
### Citation
If you use ANNarchy for your research, we would appreciate if you cite the following paper:
> Vitay J, Dinkelbach HÜ and Hamker FH (2015). ANNarchy: a code generation approach to neural simulations on parallel hardware. *Frontiers in Neuroinformatics* 9:19. [doi:10.3389/fninf.2015.00019](http://dx.doi.org/10.3389/fninf.2015.00019)
### Authors
* Julien Vitay (julien.vitay@informatik.tu-chemnitz.de).
* Helge Ülo Dinkelbach (helge-uelo.dinkelbach@informatik.tu-chemnitz.de).
* Fred Hamker (fred.hamker@informatik.tu-chemnitz.de).
## Installation
Using pip, you can install the latest stable release:
```bash
pip install ANNarchy
```
See <https://annarchy.github.io/Installation> for further instructions.
## Platforms
* GNU/Linux
* MacOS X
* Windows (inside WSL2)
## Dependencies
* `python` >= 3.10 (with the development files, e.g. `python-dev` or `python-devel`)
* `g++` >= 7.4 or `clang++` >= 3.4
* `cmake` >= 3.16
* `setuptools` >= 65.0
* `cython` >= 3.0
* `numpy` >= 1.21
* `sympy` >= 1.11
* `scipy` >= 1.9
* `matplotlib` >= 3.0
* `tqdm` >= 4.60
Recommended:
* `lxml` (to save the networks in `.xml` format).
* `h5py` (to export data in `.h5` format).
* `pandoc` (for `report()`).
* `tensorflow` (for the `ann_to_snn_conversion` extension)
* `tensorboardX` (for the `logging` extension).
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"description": "# ANNarchy \n\n[](https://zenodo.org/badge/latestdoi/57382690)\n\n\nANNarchy (Artificial Neural Networks architect) is a parallel and hybrid simulator for distributed rate-coded or spiking neural networks. The core of the library is written in C++ and distributed using openMP or CUDA. It provides an interface in Python for the definition of the networks. It is released under the [GNU GPL v2 or later](http://www.gnu.org/licenses/gpl.html).\n\n* Source code: [github.com/ANNarchy/ANNarchy](https://github.com/ANNarchy/ANNarchy)\n* Documentation: [annarchy.github.io](https://annarchy.github.io)\n* Forum: [google forum](https://groups.google.com/forum/#!forum/annarchy)\n* Bug reports and feature requests: [Issue Tracker](https://github.com/ANNarchy/ANNarchy/issues).\n\n### Citation\n\nIf you use ANNarchy for your research, we would appreciate if you cite the following paper:\n\n> Vitay J, Dinkelbach H\u00dc and Hamker FH (2015). ANNarchy: a code generation approach to neural simulations on parallel hardware. *Frontiers in Neuroinformatics* 9:19. [doi:10.3389/fninf.2015.00019](http://dx.doi.org/10.3389/fninf.2015.00019)\n\n### Authors\n\n* Julien Vitay (julien.vitay@informatik.tu-chemnitz.de).\n* Helge \u00dclo Dinkelbach (helge-uelo.dinkelbach@informatik.tu-chemnitz.de).\n* Fred Hamker (fred.hamker@informatik.tu-chemnitz.de).\n\n## Installation\n\nUsing pip, you can install the latest stable release:\n\n```bash\npip install ANNarchy\n```\n\nSee <https://annarchy.github.io/Installation> for further instructions.\n\n## Platforms\n\n* GNU/Linux\n* MacOS X\n* Windows (inside WSL2)\n\n## Dependencies\n\n* `python` >= 3.10 (with the development files, e.g. `python-dev` or `python-devel`)\n* `g++` >= 7.4 or `clang++` >= 3.4\n* `cmake` >= 3.16\n* `setuptools` >= 65.0\n* `cython` >= 3.0\n* `numpy` >= 1.21\n* `sympy` >= 1.11\n* `scipy` >= 1.9\n* `matplotlib` >= 3.0\n* `tqdm` >= 4.60\n\nRecommended:\n\n* `lxml` (to save the networks in `.xml` format).\n* `h5py` (to export data in `.h5` format).\n* `pandoc` (for `report()`).\n* `tensorflow` (for the `ann_to_snn_conversion` extension)\n* `tensorboardX` (for the `logging` extension).\n",
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