[smonn_]

I'm currently working on a variation of a spiking neural network that learns by making and purging connections between neurons, which so far has been pretty interesting, though I am having a hard time getting it to output anything more than just the patterns it recognised. I did play around with adding its outputs to the input list, making it sort of recurrent but its practically impossible to decode anything thats going on inside of the network. Im thinking of tracking the inputs around to see what its doing right now, might be interesting to see it generate some sort of tree-like structure.

[kyllo]

Are you familiar with the edge popup algorithm introduced in "What's Hidden in a Randomly Weighted Neural Network?" https://arxiv.org/abs/1911.13299v2

Seems relevant to what you're working on. It starts with a randomly initialized, overparameterized neural net, but instead of gradient descent backpropagation, it learns by deleting connection edges.

[smonn_]

I haven't read it, thanks a lot! I'm probably going to use it in an essay I'm writing about the topic.