Topic: sparse biological neuronal networks

A range of work has been done analyzing the effect of specific pruning rules on the dynamics of recurrent neural networks (RNNs) with a given connectivity structure. However, a more comprehensive study that explains the effect of different, biologically plausible pruning rules across a variety of structures has yet to be done. Specifically, previous work has investigated uniform random sparsification on RNNs with low rank connectivity matrices [1], and utilized theoretical results from graph sparsification to propose a pruning rule shown to be optimal for preserving the dynamics of symmetric a