Open-sourcing BindsNET

Notes

After a few missteps in the PyPI distribution process, we are proud to annouce the release of BindsNET v0.1! We will likely follow up with a series of incremental releases (v0.1.x) to address bugs found by users, or add small-scale features that we may have missed.

Features

This release features the network core functionality of the package, which enables the construction and simulation of spiking neural networks (SNNs). The Network object may be composed of any number of Nodes, Connections, and / or Monitors, of which there several varieties. Learning on Connection objects is implemented by specifying functions from the learning module. Popular machine learning (ML) datasets may be loaded using datasets, which can be converted into spike trains (like any other numerical data) with encoding.

An interface into the Open AI gym reinforcement learning (RL) library is implemented using the environments module, allowing for the first time easy experimentation with SNNs on RL problems.

To eliminate messy implementation details, a Pipeline object is provided (in the pipeline module) which simulates altogether the interaction between a spiking neural network and a dataset or environments. This saves users from having to write long scripts to run experiments on supported datasets or RL environments.

Plotting functionality is available in the analysis.plotting and analysis.visualization modules. The former is typically used for plotting "online" during simulation, and the latter, "offline", for studying long-term network behavior or making figures.

Other modules exist in a developmental or low-user / low-priority state.

Future work?

This depends largely on the users and in particular the needs of the BINDS lab. Some things we would personally like to see include:

• Tighter integration with PyTorch. This likely means using more functionality from the torch.nn.functional module (e.g., convolution, pooling, activation functions, etc.), or conforming our network API to that of torch's neural network API.
• Automatic smoothing of SNNs: Recent work has shown that it's possible to convert trained deep learning NNs to SNNs without much loss in accuracy. Conversion of PyTorch models or models specified in the ONNX format may be supported in BindsNET in the future!
• More features! Nodes (neuron) types, Connection types, Datasets, learning functions, and more. In particular, we want to take steps towards making SNNs robust for ML / RL.

Cheers,
@djsaunde