Accuracy on MNIST is a lousy 82.9%, 86.5%, and 87.4% for the networks with 400, 1000, and 6400 neurons, respectively -- well below the accuracy achieved by even LeNet-5, a landmark historical CNN architecture from the 1990's.[a]
Nonetheless, I get excited when I come across research like this. I get excited about the possibility of building devices with internal physical/chemical network/tree structures capable of learning in response to stimuli, e.g., by settling into low-energy states that "best model" such stimuli or via some other mechanism.
This is the kind of research, I think, that could eventually lead to AI systems operating with orders of magnitude more energy, space, time, and cost efficiency than current systems implemented as software running on GPU, TPU, and similar devices.