The figure included belowshowssample plots, comparing noise-prune toa6 control that prunes only based on weight. Figure 1: Noise-prune on non-symmetric clustered networks. Networks have dense within-cluster and sparse between-cluster connections.

We have empirically characterized noise-prune's performance on non-symmetric clustered networks (i.e., going beyond We will include an expanded set of these results in the manuscript. We have not yet characterized noise-prune's performance against these Building off of the Reviewer's language learning example, even here the dynamical patterns are We will now include some text on other benefits of sparsity in the Discussion. Blue shows equivalent curve for weight-based pruning. Noise-prune performs significantly better than pruning by weights.