KerasSpiking versus NengoDL

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 KerasSpiking and NengoDL).

The short answer is that KerasSpiking is designed to be a lightweight, minimal implementation of spiking behaviour that integrates very transparently into Keras. 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 underlying TensorFlow/Keras API. There is more of a learning curve to getting started with NengoDL, and the resulting code looks less like standard Keras code (although it is still designed to feel familiar to Keras users).

One particularly significant distinction is that KerasSpiking 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 KerasSpiking 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 KerasSpiking if you want to get up and running with minimal departures from the standard Keras 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.