[rbn3]

This instantly reminded me of the paper "pattern recognition in a bucket"[0], which I've seen referenced a lot when I first started reading about AI in general. I only have surface-level knowledge about the field, but how exactly does what's described in the article differ from reservoir computing? (The article doesn't mention that term, so I assume there must be a difference)

[0] https://www.researchgate.net/publication/221531443_Pattern_R...

[grahamrow]

In this PNN approach you are solving for what additional stimuli, when applied to the system alongside the inputs, produce the desired result for a given input. In reservoir computing (RC) you don’t bother to provide any additional stimuli, and find the linear combination of reservoir outputs that gives the desired result. Training the former is more demanding and analogous to a NN (thus the name), but directly produces your answer from the system. The latter is very easy to train (one regression) but requires post processing for inference.

[rbn3]

aaah - got it, thanks!