We demonstrate a meaningful prospective power analysis for an (admittedly idealized) illustrative connectome inference task. Modeling neurons as vertices and synapses as edges in a simple random graph model, we optimize the trade-off between the number of (putative) edges identified and the accuracy of the edge identification procedure. We conclude that explicit analysis of the quantity/quality trade-off is imperative for optimal neuroscientific experimental design. In particular, more though more errorful edge identification can yield superior inferential performance.
@article{arxiv.1108.6271,
title = {Optimizing the quantity/quality trade-off in connectome inference},
author = {Carey E. Priebe and Joshua T. Vogelstein and Davi Bock},
journal= {arXiv preprint arXiv:1108.6271},
year = {2011}
}