Data-driven models in human neuroscience and neuroengineering
Current Opinion in Neurobiology, ISSN: 0959-4388, Vol: 58, Page: 21-29
2019
- 22Citations
- 153Captures
- 2Mentions
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
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Review Description
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.
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S0959438818302502; http://dx.doi.org/10.1016/j.conb.2019.06.008; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85069045171&origin=inward; http://www.ncbi.nlm.nih.gov/pubmed/31325670; https://linkinghub.elsevier.com/retrieve/pii/S0959438818302502; https://dx.doi.org/10.1016/j.conb.2019.06.008
Elsevier BV
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