> single bit neural networks are decision trees.

I didn't exactly understood what was meant here, so I went out and read a little. There is an interesting paper called "Neural Networks are Decision Trees" [1]. Thing is, this does not imply a nice mapping of neural networks onto decision trees. The trees that correspond to the neural networks are huge. And I get the idea that the paper is stretching the concept of decision trees a bit.

Also, I still don't know exactly what you mean, so would you care to elaborate a bit? :)

[1] https://arxiv.org/pdf/2210.05189

Closest thing I found was:

Single Bit Neural Nets Did Not Work - https://fpga.mit.edu/videos/2023/team04/report.pdf

> We originally planned to make and train a neural network with single bit activations, weights, and gradients, but unfortunately the neural network did not train very well. We were left with a peculiar looking CPU that we tried adapting to mine bitcoin and run Brainfuck.

> I still don't know exactly what you mean

Straight forward quantization, just to one bit instead of 8 or 16 or 32. Training a one bit neural network from scratch is apparently an unsolved problem though.

> The trees that correspond to the neural networks are huge.

Yes, if the task is inherently 'fuzzy'. Many neural networks are effectively large decision trees in disguise and those are the ones which have potential with this kind of approach.