Note

This documentation is for a development version. Click here for the latest stable release (v0.1.0).

PyTorchSpiking versus NengoDLΒΆ

If you are interested in combining spiking neurons and deep learning methods, you may be familiar with NengoDL (and wondering what the difference is between PyTorchSpiking and NengoDL).

The short answer is that PyTorchSpiking is designed to be a lightweight, minimal implementation of spiking behaviour that integrates very transparently into PyTorch. It is designed to get you up and running on building a spiking model with very little overhead.

NengoDL provides much more robust, fully-featured tools for building spiking models. More neuron types, more synapse types, more complex network architectures, more of everything basically. However, all of those extra features require a more significant departure from the PyTorch API. There is more of a learning curve to getting started with NengoDL, and because NengoDL is based on TensorFlow/Keras, the API is designed to be more familiar to those with Keras experience.

One particularly significant distinction is that PyTorchSpiking does not really integrate with the rest of the Nengo ecosystem (e.g., it cannot run models built with the Nengo API, and models built with PyTorchSpiking cannot run on other Nengo platforms). In contrast, NengoDL can run any Nengo model, and models optimized in NengoDL can be run on other Nengo platforms (such as custom neuromorphic hardware, like NengoLoihi).

In summary, you should use PyTorchSpiking if you want to get up and running with minimal departures from the standard PyTorch API. If you find yourself wishing for more control or more features to build your model, or you would like to run your model on different hardware platforms, consider checking out NengoDL.