Optimizing the quantity/quality trade-off in connectome inference

TL;DR

This paper presents a power analysis framework for connectome inference, showing that balancing the number and accuracy of identified edges can optimize neuroscientific experiments.

Contribution

It introduces a method to optimize the trade-off between edge quantity and quality in connectome inference using a simple random graph model.

Findings

More errorful edge identification can improve inference performance.

Explicit trade-off analysis is crucial for optimal experimental design.

Power analysis guides better resource allocation in connectome studies.

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.