This repository contains introductory level SNN examples that I built to understand more about the neuron models and the main network types by using sPyNNaker (PyNN for SpiNNaker).
- sPyNNaker8*
- matplotlib (2.2.3)
- PyNN (0.9.3)**
- numpy (1.16.0)
Along with Python 2.7.
spynnaker-examples/
├── compare_voltages.py
├── empty_snn_pattern.py
├── izhikevich_snn.py
├── one_neuron_if_curr_exp.py
├── one_neuron_izhikevich.py
├── recurrent_network.py
├── scripts
│ ├── install_spynnaker.sh
│ └── uninstall_spynnaker.sh
├── snn_if_cond_exp.py
├── snn_if_curr_alpha.py
├── snn_if_curr_exp.py
└── util
├── basic_visualizer.py
└── __init__.py
Each python file could be run separately except basic_visualizer.py and empty_snn_pattern.py.
Folder util includes utility library basic_visualizer which contains plotting functions.
Folder scripts* includes bash scripts to install and uninstall pip based sPyNNaker packages.
empty_snn_pattern.py is a pattern python file which is reproducible by filling the sections. I built this as a template for my codes.
Comparisons of neuron models in sPyNNaker8 with default values, reference compare_voltages.py.
SpiNNaker neuromorphic hardware resource is used in this project. Please refer to SpiNNaker project page regarding its setup and usage.
Notes
*There is also a python package of sPyNNaker available on pip. However, the GitHub repository of the sPyNNaker is more comprehensive and up-to-date than the pip version. If you'd lke to install sPyNNaker via pip, you may refer to my scripts in the scripts folder.
**You can run these examples on a different neuromorphic hardware resource or a simulator as well. Please see PyNN documentation for alternatives.