# Rockpool
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![Noodle](https://aictx.gitlab.io/rockpool/_static/noodle-small.png)
Rockpool is a Python package for developing signal processing applications with spiking neural networks. Rockpool allows you to build networks, simulate, train and test them, deploy them either in simulation or on event-driven neuromorphic compute hardware. Rockpool provides layers with a number of simulation backends, including Brian2, NEST, Torch, JAX, Numba and raw numpy. Rockpool is designed to make machine learning based on SNNs easier. It is not designed for detailed simulation of biological networks.
# Documentation and getting started
The best place to start with Rockpool is the [documentation](https://aictx.gitlab.io/rockpool), which contains several tutorials and getting started guides.
The documentation is hosted on GitLab: [https://aictx.gitlab.io/rockpool/](https://aictx.gitlab.io/rockpool/)
# Installation instructions
Use `pip` to install Rockpool and required dependencies
```bash
$ pip install rockpool --user
```
The `--user` option installs the package only for the current user.
If you want to install all the extra dependencies required for Brian, PyTorch and Jax layers, use the command
```bash
$ pip install rockpool[all] --user
```
## NEST layers
The NEST simulator cannot be installed using `pip`. Please see the documentation for NEST at [https://nest-simulator.readthedocs.io/en/latest/] for instructions on how to get NEST running on your system.
# License
Rockpool is released under a AGPL license. Commercial licenses are available on request.
# Contributing
Fork the public repository at https://gitlab.com/ai-ctx/rockpool, then clone your fork.
```bash
$ git clone https://gitlab.com/your-fork-location/rockpool.git rockpool
```
Install the package in development mode using `pip`
```bash
$ cd rockpool
$ pip install -e . --user
```
or
```bash
$ pip install -e .[all] --user
```
The main branch is `development`. You should commit your modifications to a new feature branch.
```bash
$ git checkout -b feature/my-feature develop
...
$ git commit -m 'This is a verbose commit message.'
```
Then push your new branch to your repository
```bash
$ git push -u origin feature/my-feature
```
When you're finished with your modifications, make a merge request on gitlab.com, from your branch in your fork to https://gitlab.com/ai-ctx/rockpool.
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rockpool-1.0.7.post1.tar.gz (71个子文件)
rockpool-1.0.7.post1
PKG-INFO 4KB
setup.cfg 38B
setup.py 1KB
README.md 3KB
rockpool
networks
internal
__init__.py 0B
__init__.py 2KB
network.py 43KB
gpl
__init__.py 0B
net_deneve.py 8KB
net_rate_reservoir.py 6KB
utilities
internal
__init__.py 0B
__init__.py 2KB
gpl
timedarray_shift.py 13KB
property_arrays.py 11KB
__init__.py 0B
type_handling.py 656B
analysis.py 1KB
__init__.py 2KB
timeseries.py 93KB
layers
layer.py 26KB
training
internal
__init__.py 0B
__init__.py 2KB
gpl
rr_trained_layer.py 12KB
__init__.py 0B
train_rr.py 7KB
train_jax_sgd.py 8KB
internal
training
__init__.py 0B
__init__.py 0B
__init__.py 3KB
gpl
lif_jax.py 38KB
rate.py 36KB
updown.py 17KB
event_pass.py 4KB
spike_bt.py 21KB
pytorch
iaf_torch.py 87KB
torch_cnn_layer.py 8KB
__init__.py 0B
exp_synapses_torch.py 33KB
rate_jax.py 24KB
exp_synapses_manual.py 25KB
__init__.py 0B
butter_mel_filter.py 7KB
iaf_brian.py 64KB
devices
params.py 1KB
__init__.py 0B
virtual_dynapse.py 63KB
dynap_hw.py 42KB
aeif_nest.py 17KB
iaf_nest.py 53KB
dynapse_brian.py 10KB
exp_synapses_brian.py 9KB
iaf_digital.py 24KB
softmaxlayer.py 3KB
iaf_cl.py 33KB
devices
server.py 183B
params.py 1KB
tools.py 27KB
rpyc_headless.py 706B
NeuronNeuronConnector.py 21KB
__init__.py 2KB
dynapse_control_extd.py 15KB
dynapse_control.py 107KB
weights
__init__.py 2KB
gpl
__init__.py 0B
reservoirweights.py 47KB
version.py 28B
rockpool.egg-info
PKG-INFO 4KB
requires.txt 128B
SOURCES.txt 2KB
top_level.txt 9B
dependency_links.txt 1B
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