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[P] PySNN: Spiking Neural Network framework, built on top of PyTorch

Hi everyone!

Recently a friend and I have been working on a new Python library for machine learning with Spiking Neural Networks (SNNs), called PySNN, which is built on top of PyTorch. We feel it is time to share it with more people, and hopefully get good feedback and contributions:)!

Our goal for PySNN was to make a truly modular framework for machine learning with SNNs, while staying as close to PyTorch as possible. All of the existing frameworks either operate more like simulators for neuroscientific research, or use relatively fixed network and training/evaluation loop designs. PySNN, on the other hand, consists of building blocks for neurons, connections, and learning rules which the user can combine in their desired way. It even allows for mixing learning rules or training only specific parts of the network.

Furthermore, since PySNN consists of just the basic elements, the framework is lightweight and allows for easy extension. Because of its tight integration with PyTorch it fully supports GPU acceleration, batching of samples, and supports tools like the jit compiler and graph tracing for TensorBoard.

There are still many improvements and extensions that can be made, so feel free to have a look and send out a pull request! We will be very active in helping with any issues!

We are looking forward to your comments and suggestions!:)

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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.