ANNarchy


NameANNarchy JSON
Version 4.8.2 PyPI version JSON
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SummaryArtificial Neural Networks architect
upload_time2024-11-20 18:23:48
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requires_python>=3.10
licenseGPLv2+
keywords neural simulator
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            # ANNarchy 

[![DOI](https://zenodo.org/badge/57382690.svg)](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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