Welcome to SINABS’s documentation!

SINABS

Getting started

Sinabs Is Not A Brain Simulator

sinabs is a python library for development and implementation of Spiking Convolutional Neural Networks (SCNNs). The library implements several layers that are spiking equivalents of CNN layers. In addition it provides support to import CNN models implemented in torch conveniently to test their spiking equivalent implementation. This project is managed by SynSense (former aiCTX AG).

NOTE: The conversion of CNNs to SCNNs is still a subject of research and we strive to keep the library updated to the state-of-the art in addition to providing options to compare various approaches both at a high level abstraction to low level implementation details.

NOTE: This library is in Beta release stage and is subject to API changes.

Installation

You can install sinabs with pip:

pip install sinabs

Checkout our quick instructional on how to create a project based on sinabs within a virtual environment using pyenv+pipenv

If you want to develop or have access to source code of sinabs, download the package from the git repository:

$ cd <to/your/software/folder>
$ git clone https://gitlab.com/aiCTX/sinabs.git>
$ cd sinabs
$ pip install -e . --user

For developers, we recommend that you install this package as a development version so that you can update the package without reinstalling the package.

Documentation and Examples

https://sinabs.ai

If you would like to generate documentation locally, you can do that using sphinx.

REQUIREMENT You will require pandoc installed on your system.

You can generate a sphinx documentation for this package by running the the following command.

$ cd /path/to/sinabs/
$ pip install -r sphinx-requirements.txt
$ python setup.py build_sphinx

This will build and auto generate html documentation at docs/build/html/index.html You can access the generated documentation in your browser.

$ firefox docs/build/html/index.html

License

sinabs is published under AGPL v3.0. See the LICENSE file for details.

Contributing to sinabs

Checkout CONTRIBUTING.md

Table of contents

• SINABS
  • Getting started
  • Installation
  • Documentation and Examples
  • License
  • Contributing to sinabs
• Introduction
  • A quick (and over-simplified) introduction to spiking neurons
• Examples
  • MNIST Example
  • Training by backpropagation through time (BPTT)
• How To Guide
  • Generate rate-based spike trains from normalised float number
  • Python versions, pyenv and pipenv
• API
  • sinabs
  • sinabs.layers
  • sinabs.layers.functional
  • sinabs.from_torch
  • sinabs.SynOpCounter
• Contributing to sinabs
  • How do you go about it?
  • Coding style
  • Testing

Indices and tables

• Index

• Module Index