We develop brain-machine interfaces to restore, replace, and augment nervous system function, with a particular focus on movement disorders. We use engineering approaches to leverage neural plasticity for improved interfaces, and use engineered interfaces as tools to study neural mechanisms of learning.
multi-learner adaptive systems
computation in neural networks
multi-scale, multi-modal and large-scale neural recording technologies
system integration for advancing neurotechnologies
The laboratory works at the intersection of engineering and neuroscience to develop therapeutic neural interfaces. The lab explores neural interfaces as adaptive closed-loop systems that engage neural plasticity and adaptation. We use engineering approaches to leverage neural adaptation for system performance, and uses neural interfaces as a tool to study neural mechanisms of learning in circuits. The lab also specializes in system integration for advancing neurotechnologies to study neural circuits in awake primates for basic science and towards human translation. We use state of the art techniques to study neural circuits during behavior, including: optogenetics, integrated multi-scale and multi-modal neural measurements/manipulations, large-scale recordings, wireless in-cage recordings, high-dimensional motion tracking, and closed-loop adaptive interfaces.
Clare Boothe Luce professorship, 2018 – 2024
L’Oréal USA for Women in Science postdoctoral fellowship, 2016
1st runner up, Rosalind Franklin Appathon “Best New App” for promoting women in
International Brain-Computer Interface Meeting student travel fellowship, 2013
American Heart Association, Western States Affiliate, Pre-doctoral fellowship, 2011
National Science Foundation Graduate Research Fellowship, 2008
Outstanding Senior in Engineering Physics, CWRU, 2007
Krumhansle Family Prize for Outstanding Achievement in Physics, CWRU, 2006 and 2007
Tau Beta Pi engineering honor society, 2005
Fiat Awards Program Scholarship, 2004 and 2008
Case Western Reserve University Provost Scholarship, 2003 – 2007
J. Kleinbart, A. L. Orsborn, John S. Choi, C. Wang, S. Qiao, J. Viventi, B. Pesaran (2018) A modular implant system for multimodal recording and manipulation of the primate brain, 39th International conference IEEE EMBS, Honolulu, HI.
M. Shanechi*, A. L. Orsborn* (equal contribution), H.G. Moorman*, S. Gowda*, and J.M. Carmena (2017). Rapid control and feedback rates enhance neuroprosthetic control. Nature Communications, 8:13825, doi:10.1038/ncomms13825
A.L. Orsborn and B. Pesaran (2017) Parsing learning in networks using brain-machine interfaces, Current Opinions in Neurobiology, 46:76-83, doi: 10.1016/j.conb.2017.08.002
M. Shanechi , A.L. Orsborn* (equal contribution), and J.M. Carmena (2016). Robust brain-machine interface design using optimal feedback control modeling and adaptive point process filtering. PLoS Computational Biology 12(4):e1004730. doi:10.1371/journal.pcbi.1004730 (F1000 recommended)
A.L. Orsborn, K. So, S. Dangi, and J.M. Carmena (2013) Comparison of neural activity during closed-loop control of spike- or LFP-based brain-machine interfaces. Proceedings of the 6th International Conference IEEE EMBS Neural Engineering, San Diego, CA.
A.L. Orsborn, H.G. Moorman, S.A. Overduin, M. M. Shanechi, D. Dimitrov, and J.M. Carmena (2014) Closed-loop decoder adaptation shapes neural plasticity for skillful neuroprosthetic control, Neuron 82, pp. 1380-1393. (journal cover article)
A.L. Orsborn and J.M. Carmena (2013) Creating new functional circuits for action via brain-machine interfaces, Frontiers in Computational Neuroscience, 7:157, doi: 10.3389/fncom.2013.00157
S. Dangi*, A.L. Orsborn* (equal contribution), H.G. Moorman, and J.M. Carmena (2013) Design and analysis of closed-loop decoder adaptation algorithms for brain-machine interfaces. Neural Computation, 25(7), pp. 1693-1731.
A.L. Orsborn, S. Dangi, H.G. Moorman, and J.M. Carmena (2012) Closed-loop decoder adaptation on intermediate time-scales facilitates rapid BMI performance improvements independent of decoder initialization conditions. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 20(4), pp. 468 – 477.