I've also seen implementations of realistic neurons, spiking models, etc. In software implementations, what combo of libraries and hardware would equal your 200,000 biological neurons in performance (esp training)? How many GPU's are we talking about?
(Note: If you haven't already, it might be helpful to publish a stack like that so people can experiment with encodings or reinforcement methods at no cost to you.)
We focus on using real neurons, I'm not aware of a software based equivalent. But users can `pip install cl-sdk` to get started with our API. The SDK is still early but supports playing back a recording of real data so applications can be built with a realistic spike frequency. (We'll be releasing a set of recordings for this)
The closest thing to real neurons are called spiking, neural networks. Here's a list of libraries for building them on GPU's:
https://github.com/open-neuromorphic/open-neuromorphic
For academic papers, the phrases they use include "biologically plausible," "Hebbian learning," and "backpropagation free." Searching for those with "neural networks" or "neurons" will turn up cutting-edge techniques.