[dqh]

Cortical Labs CTO here. My focus is on the system itself rather than applications, but for what it's worth ..

When the neurons didn't get stimulated by the application, performance did not improve. The only explanation our data science people has is that the neurons began to learn and perform the desired (highly abstracted) task of 'playing Doom'. This was not a surprise as we've shown this before with a version of Pong using a different platform. We built the CL1 and the CL API to enable rapid iteration on this sort of work.

One benefit to this is that when you have a measurable learning effect, you can measure this before and after exposure to an experimental drug or other molecule. It becomes possible to test the impact on neuron function, not just survival.

[nickpsecurity]

It was a great video. You're doing interesting work.

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.)

[dqh]

Thanks!

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)

[nickpsecurity]

Thanks for thr tip.

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.

[dqh]

We'll likely leave the answering of your equivalence question to our users. While we do plenty of internal research, the primary goal of our cloud platform is to dramatically expand access to this field. There are no doubt many low-hanging fruits to be discovered.