Data-driven models in human neuroscience and neuroengineering

Bingni W. Brunton, Michael Beyeler Current Opinion in Neurobiology 58:21-29

Data-driven models in human neuroscience and neuroengineering


Discoveries in modern human neuroscience are increasingly driven by quantitative understanding of complex data. Data-intensive approaches to modeling have promise to dramatically advance our understanding of the brain and critically enable neuroengineering capabilities. In this review, we provide an accessible primer to modern modeling approaches and highlight recent data-driven discoveries in the domains of neuroimaging, single-neuron and neuronal population responses, and device neuroengineering. Further, we suggest that meaningful progress requires the community to tackle open challenges in the realms of model interpretability and generalizability, training pipelines of data-fluent human neuroscientists, and integrated consideration of data ethics.

In the News

Prof. Beyeler was mentioned in a recent article by The Guardian.