Optimizing the quantity/quality trade-off in connectome inference

2011/08/31 by Carey E. Priebe, Joshua T. Vogelstein, Priebe, Carey E. +3
Biochemistry, Genetics and Molecular Biology · Mathematics · #Applications (stat.AP) #FOS: Biological sciences #FOS: Computer and information sciences #Neurons and Cognition (q-bio.NC) #q-bio.NC #stat.AP

paper · pdf · doi:10.48550/arxiv.1108.6271

8 pages, 1 figure

arxiv created 2011/10/12 · arxiv updated 2011/10/13

Abstract

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.

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