The brain modifies its synaptic strengths during learning in order to better adapt to its environment. However, the underlying plasticity rules that govern learning are unknown. Many proposals have been suggested, including Hebbian mechanisms, explicit error backpropagation, and a variety of alternatives. It is an open question as to what specific experimental measurements would need to be made to determine whether any given learning rule is operative in a real biological system. In this work, we take a "virtual experimental" approach to this problem.

Weaknesses: This paper has stuck with me, and I do want to emphasize just how interesting I find it. I am very much in favor of it, but the following list of weaknesses is holding me back from backing its acceptance. Broadly, I need more convincing that (1) discrimination is not trivially due to differences in learning alg performance, (2) how learning algorithm vs. architecture can ever be dissociated in model organisms, and (3) *why* would differences at the level of weights (fig 3) be indicative of different learning algorithms in a way that cannot be deduced via first principles (related to point 2). I am suspicious that the ability to discriminate between learning algorithm is driven by differences in their performance on imagenet. While it's not obvious to me *how* this would work, it seems plausible that learning algorithm differences e.g. I think that the authors need to control for this.

All four referees support accept. In general, reviewers agree that this submission is extremely novel and highly relevant to both neuroscience and ML researchers alike. Author response convincingly addressed concerns several reviewers raised about experiments. Thus, I strongly recommend accept, but encourage authors to incorporate reviewer feedback in camera ready.

The brain modifies its synaptic strengths during learning in order to better adapt to its environment. However, the underlying plasticity rules that govern learning are unknown. Many proposals have been suggested, including Hebbian mechanisms, explicit error backpropagation, and a variety of alternatives. It is an open question as to what specific experimental measurements would need to be made to determine whether any given learning rule is operative in a real biological system. In this work, we take a "virtual experimental" approach to this problem.