title: Making brain-machine interfaces robust to future neural variability.
abstract: A major hurdle to clinical translation of brain-machine interfaces (BMIs) is that current decoders, which are trained from a small quantity of recent data, become ineffective when neural recording conditions subsequently change. We tested whether a decoder could be made more robust to future neural variability by training it to handle a variety of recording conditions sampled from months of previously collected data as well as synthetic training data perturbations. We developed a new multiplicative recurrent neural network BMI decoder that successfully learned a large variety of neural-to-kinematic mappings and became more robust with larger training data sets. Here we demonstrate that when tested with a non-human primate preclinical BMI model, this decoder is robust under conditions that disabled a state-of-the-art Kalman filter-based decoder. These results validate a new BMI strategy in which accumulated data history are effectively harnessed, and may facilitate reliable BMI use by reducing decoder retraining downtime.
authors.affiliation: Electrical Engineering Department, Stanford University, Stanford, California 94305, USA.|Stanford Neurosciences Institute, Stanford, California 94305, USA.
authors.forename: David
authors.initials: D
authors.lastname: Sussillo
authors.affiliation: Neurosciences Graduate Program, Stanford, California 94305, USA.
authors.forename: Sergey D
authors.initials: SD
authors.lastname: Stavisky
authors.affiliation: Electrical Engineering Department, Stanford University, Stanford, California 94305, USA.
authors.forename: Jonathan C
authors.initials: JC
authors.lastname: Kao
authors.affiliation: Electrical Engineering Department, Stanford University, Stanford, California 94305, USA.|Palo Alto Medical Foundation, Palo Alto, California 94301, USA.
authors.forename: Stephen I
authors.initials: SI
authors.lastname: Ryu
authors.affiliation: Electrical Engineering Department, Stanford University, Stanford, California 94305, USA.|Stanford Neurosciences Institute, Stanford, California 94305, USA.|Neurosciences Graduate Program, Stanford, California 94305, USA.|Neurobiology and Bioengineering Departments, Stanford, California 94305, USA.|Bio-X Program, Stanford, California 94305, USA.|Howard Hughes Medical Institute at Stanford University Stanford University, Stanford, 94305 California, USA.
authors.forename: Krishna V
authors.initials: KV
authors.lastname: Shenoy
mesh_terms: D000818:Animals; D001931:Brain Mapping; D062207:Brain-Computer Interfaces; D008253:Macaca mulatta; D008297:Male; D009415:Nerve Net