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Learning an Internal Dynamics Model from Control Demonstration.

, , and . ICML (1), volume 28 of JMLR Workshop and Conference Proceedings, page 606-614. JMLR.org, (2013)

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Neural constraints on learning., , , , , , , and . Nature, 512 (7515): 423-426 (2014)Neural manifolds: from basic science to practical improvements in brain-computer intefaces.. BCI, page 1-2. IEEE, (2019)A stabilized dual Kalman filter for adaptive tracking of brain-computer interface decoding parameters., and . EMBC, page 7100-7103. IEEE, (2013)Bayesian learning in assisted brain-computer interface tasks., , , and . EMBC, page 2740-2743. IEEE, (2012)Learning an Internal Dynamics Model from Control Demonstration., , and . ICML (1), volume 28 of JMLR Workshop and Conference Proceedings, page 606-614. JMLR.org, (2013)Functional network reorganization in motor cortex can be explained by reward-modulated Hebbian learning., , , and . NIPS, page 1105-1113. Curran Associates, Inc., (2009)Recasting brain-machine interface design from a physical control system perspective., and . J. Comput. Neurosci., 39 (2): 107-118 (2015)Dimensionality reduction of calcium-imaged neuronal population activity., , , , , , , , , and 1 other author(s). Nat. Comput. Sci., 3 (1): 71-85 (2023)Comparison of brain-computer interface decoding algorithms in open-loop and closed-loop control., , , , , and . J. Comput. Neurosci., 29 (1-2): 73-87 (2010)A control-theoretic approach to brain-computer interface design., and . ACC, page 5765-5771. IEEE, (2016)