My guess would be that the structure to allow for plasticity to work is a strong limit to how well you can tune weights to fit training data. My (doubtless massively oversimplified and way behind the state-of-the art) understanding of neuro-plasticity is something like "often used pathways get stronger connections". Which, even with significant tweaks, is quite different from the gradient descent used to tune neural networks.
It argues that our understanding of neurons storing information in superposition was just a limitation of our tools and us thinking about it wrong. Instead most neurons initially specialize, and then branch out into related concepts. In addition, understanding one part of the network helps better understand other parts. And with agents we can scale this to understand non-trivial networks.
While, if true, this would more directly help with interpretability, a better understanding could also help with improving NNs in general.
Why do you think this is? I am very new to this research, but to me, the biggest challenge appears to be how information seems to be stored in superposition (making it difficult to change one thing without affecting others). But biological brains appear to exhibit the same kind of superposition storage as well, and yet they are very plastic.