tracetorch 0.1.1

An extension to PyTorch: SNN layers that function on traces.

traceTorch is a PyTorch-based library built on the principles of spiking neural networks, replacing the PyTorch default backpropagation through time with lightweight, per-layer input traces, enabling biologically inspired, constant time and memory consumption learning on arbitrarily long or even streaming sequences.

Documentation

It is highly recommended that you read the documentation first. It contains:

1. Introduction: An introduction to traceTorch, how and why it works, it's founding principles. It's thoroughly recommended that you read through the entire introduction and gain an intuitive understanding before proceeding.
2. Tutorials: Various tutorials to create your own traceTorch models. The resultant code can be found in tutorials/.
3. Documentation: The actual documentation to all the modules included in traceTorch. It includes detailed explanations, examples and math to gain a full understanding.

Roadmap

• Create the poisson click test example
• Implement the trace alternative to REINFORCE
• Finish writing the documentation
• Move tutorial code to separate repository
• Implement abstract graph based models, not just sequential

Installation

traceTorch is a PyPI library, which can be found here.

You can install it via pip. All the required packages for it to work are also downloaded automatically.

pip install tracetorch

To use, simply do:

import tracetorch

Usage examples

tutorials/ contains all the tutorial files, ready to run and playtest. The tutorials themselves can be found here.

The tutorials make use of libraries that tracetorch doesn't necessarily use. To ensure that you have all the necessary packages for the tutorials installed, please install the packages listed in tutorials/requirements.txt

cd tutorials/
pip install -r requirements.txt

It's recommended to use an environment that does not have tracetorch installed if using the tutorials, tracetorch/ is structured identically to the library, but is of course a running release.

Authors

• @Yegor-men

Acknowledgements

I built traceTorch from the ground up, trying to reverse engineer biological neurons with a sprinkle of intelligent design, but I would also like to recognize the following projects and people who helped shape my thinking:

• snntorch for introducing me to SNN networks in the first place, and their principles of function. Ironically, its dependency on constructing the full autograd graph is what largely inspired me to make traceTorch.
• Artem Kirsanov for introducing me to computational neuroscience, presenting interesting concepts in an easy-to-understand manner. My earliest tests, when I naively wanted to implement 1:1 biological neurons, largely revolved around his work.
• e-prop (eligibility propagation) inspired the whole "trace" concept, the idea of keeping a decaying value. Earlier, before traceTorch, I wanted to use e-prop for online learning instead. Admittedly unsuccessful in my attempts, and a little put off by the relative difficulty, I instead wanted to make something simpler.

Contributing

Contributions are always welcome. Feel free to submit pull requests or report issues, I will occasionally check in on it.

You can also reach out to me via either email or Twitter:

• [e-mail]
• Twitter