Review for NeurIPS paper: A meta-learning approach to (re)discover plasticity rules that carve a desired function into a neural network

Additional Feedback: Given that analytical derivations of Oja-like learning rules (considered in the first two problems) exist, the implication of their work for neuroscience is not clear. Such analytical derivations from principled objective functions that reflect computational tasks are not subject to the limitations of numerical optimization reported here such as having only a few input or output channels (in contrast to thousands in a biological neural network). Is their optimization algorithm modeling the evolution of learning rules? If yes, they should discuss this. If not, how can it be used to gain insight into brain computation?