A roadmap to navigate the future of neural engineering
Review article, 2026
A group of leaders in neural engineering collaborated to develop a roadmap to navigate the future of neural engineering. We covered a range of themes, including brain machine interfaces, neural modeling, artificial intelligence and machine learning, neural interfaces, neural imaging, augmented rehabilitation, and neuromaterials. For each topic we reviewed the current status, identified current and future challenges, and speculated on the emerging and necessary advances in science and technology to meet these challenges. Neural engineering will continue to yield the approaches and insights that advance the diagnosis and treatment of nervous system disorders, as well as provide new understanding of neural function.
neuroengineering
consensus
roadmap
neural engineering
Author
Warren M. Grill
Duke University
Cynthia A. Chestek
University of Michigan
Yiwen Wang
Hong Kong University of Science and Technology
Aman S. Aberra
Dartmouth College
Alain Destexhe
University Paris-Saclay
Rosa H.M. Chan
City University of Hong Kong
Bao Liang Lu
Shanghai Jiao Tong University
Yael Hanein
Tel Aviv University
Jacob T. Robinson
Rice University
Clarissa Whitmire
University of Queensland
Allen Song
Duke University
Dario Farina
Imperial College London
Daniel P. Ferris
University of Florida
Rylie A. Green
Imperial College London
Josef Goding
Imperial College London
Maria Asplund
Chalmers, Microtechnology and Nanoscience (MC2), Electronics Material and Systems
Journal of Neural Engineering
1741-2560 (ISSN) 17412552 (eISSN)
Vol. 23 4 041501Subject Categories (SSIF 2025)
Artificial Intelligence
Networked, Parallel and Distributed Computing
DOI
10.1088/1741-2552/ae78c2
PubMed
42246470