Inference in the age of big data: Future perspectives on neuroscience
Résumé
Neuroscience is undergoing faster changes than ever before. Over 100 years our field
qualitatively described and invasively manipulated single or few organisms to gain
anatomical, physiological, and pharmacological insights. In the last 10 years neuroscience
spawned quantitative datasets of unprecedented breadth (e.g., microanatomy, synaptic
connections, and optogenetic brain-behavior assays) and size (e.g., cognition, brain imaging,
and genetics). While growing data availability and information granularity have been amply
discussed, we direct attention to a less explored question: How will the unprecedented data
richness shape data analysis practices? Statistical reasoning is becoming more important to
distill neurobiological knowledge from healthy and pathological brain measurements. We
argue that large-scale data analysis will use more statistical models that are non-parametric,
generative, and mixing frequentist and Bayesian aspects, while supplementing classical
hypothesis testing with out-of-sample predictions.
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