The search for biologically faithful synaptic plasticity rules has resulted in a large body of models. They are usually inspired by -- and fitted to -- experimental data, but they rarely produce neural dynamics that serve complex functions. These failures suggest that current plasticity models are still under-constrained by existing data. Here, we present an alternative approach that uses meta-learning to discover plausible synaptic plasticity rules. Instead of experimental data, the rules are constrained by the functions they implement and the structure they are meant to produce.

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?

Despite the reviewers are still concerned with respect to some issues, like the power of discovering new plasticity rules or the lack of empirical evidence on real data, the rebuttal provided a satisfactorily answer to the criticism of scalability.

The search for biologically faithful synaptic plasticity rules has resulted in a large body of models. They are usually inspired by -- and fitted to -- experimental data, but they rarely produce neural dynamics that serve complex functions. These failures suggest that current plasticity models are still under-constrained by existing data. Here, we present an alternative approach that uses meta-learning to discover plausible synaptic plasticity rules. Instead of experimental data, the rules are constrained by the functions they implement and the structure they are meant to produce.