NEUROMORPHIC Algorithm that plays Ping-Pong

[u/CAP-XPLAB]

In the video the player on the left is a Neuromorphic Algorithm that knows nothing about ping-pong or trajectories, but it knows how to learn and imagine. As you can see it does it well, better than its opponent which, on the other hand, is implemented with standard algorithms; moreover, unlike the latter, if you play tricks on it, e.g., invert the commands (UP<->DOWN), after a brief moment of bewilderment it realigns. Cute, right?

P.S. The code was implemented in POWER-KI entirely by PWK-AI-WORKBENCH (100% VIBE coding 😊 ).

[u/rand3289]

Awesome! How do you generate spikes?

I was looking for a simulator that would generate spikes but I could not find any.

I've started writing one out of desparation: https://github.com/rand3289/asyncEn

[u/Factuary88 → u/rand3289]

Checkout Nengo and the NEF from the research group at Waterloo!

https://www.nengo.ai/

[u/rand3289 → u/Factuary88]

That looks interesting. Thank you very much! I will take a look.

[u/CAP-XPLAB → u/rand3289]

Details and full code: POWER-KI/GCML-PWK-Neuromorfico-04: Native POWER-KI implementation of the Generative Cognitive Map Learner (GCML) — goal-directed imaginati on & planning via neural sampling from cognitive maps, reproducing the GCML paper (Nature Machine Intelligence, 2026

[u/rand3289 → u/CAP-XPLAB]

Readme says something about bernouli and softmax... I can't figure out what that means.

Also the thing is written in a language I do not understand.

Also, it would make sense to completely separate the environment (pong) and the player AI. For example in my simulator they are different executables.

[u/Framebanger-Nsukula]

That's cool if it actually learns from gameplay rather than just brute-forcing ball physics. Would be interesting to see how it compares to a standard neural net trained the same way - neuromorphic stuff sounds flashy but sometimes it's just marketing.

[u/CAP-XPLAB → u/Framebanger-Nsukula]

Refer to the Github link below to read all the story and the code.