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Interesting, I haven't seen this twin approach anywhere in ANNs. (I know about Barlow twins and Siamese nets, but this is different.)

How did you decide on the topology of the network/graph?

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Since we have no global processor, each edge is changing using only local information, a lot of stuff that makes sense in our network doesn't really make sense in ANNs and vice versa. For example, in our newest paper (https://arxiv.org/abs/2201.04626) we desynchronize the updates of our edges. Instead of changing the entire system all at once, we change random parts of it each training step. This doesn't really make sense to do in an ANN where you require global information for every edge update.

The shape of the network is actually inspired by jamming solids (we're a soft matter lab), but is completely arbitrary. We've done a ton of different shapes and sizes in simulation.

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