Optimizing the quantity/quality trade-off in connectome inference.

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Communications in Statistics-Theory and Methods. 2013 Oct;42:3455-62. doi: 10.1080/03610926.2011.630768

Optimizing the quantity/quality trade-off in connectome inference. Bock Lab

Priebe CE, Vogelstein J, Bock D

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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, identifying edges faster/more cheaply, but with more error, can yield superior inferential performance.

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Optimizing the quantity/quality trade-off in connectome inference.

Abstract