Title
Computer-Aided Experiment Planning toward Causal Discovery in Neuroscience.
Abstract
Computers help neuroscientists to analyze experimental results by automating the application of statistics; however, computer-aided experiment planning is far less common, due to a lack of similar quantitative formalisms for systematically assessing evidence and uncertainty. While ontologies and other Semantic Web resources help neuroscientists to assimilate required domain knowledge, experiment planning requires not only ontological but also epistemological (e.g., methodological) information regarding how knowledge was obtained. Here, we outline how epistemological principles and graphical representations of causality can be used to formalize experiment planning toward causal discovery. We outline two complementary approaches to experiment planning: one that quantifies evidence per the principles of convergence and consistency, and another that quantifies uncertainty using logical representations of constraints on causal structure. These approaches operationalize experiment planning as the search for an experiment that either maximizes evidence or minimizes uncertainty. Despite work in laboratory automation, humans must still plan experiments and will likely continue to do so for some time. There is thus a great need for experiment-planning frameworks that are not only amenable to machine computation but also useful as aids in human reasoning.
Year
DOI
Venue
2017
10.3389/fninf.2017.00012
FRONTIERS IN NEUROINFORMATICS
Keywords
Field
DocType
epistemology,experiment planning,research map,causal graph,uncertainty quantification,information gain
Ontology,Data mining,Causality,Computer science,Semantic Web,Artificial intelligence,Operationalization,Management science,Ontology (information science),Causal structure,Domain knowledge,Rotation formalisms in three dimensions,Machine learning
Journal
Volume
ISSN
Citations 
11
1662-5196
1
PageRank 
References 
Authors
0.35
10
5
Name
Order
Citations
PageRank
Nicholas J. Matiasz110.35
Justin Wood210.35
Wei Wang37122746.33
Alcino J. Silva411.03
William Hsu510113.30