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A major hurdle to clinical translation of brain-machine interfaces (BMIs) isthat current decoders, which are trained from a small quantity of recent data,become ineffective when neural recording conditions subsequently change. Wetested whether a decoder could be made more robust to future neural variabilityby training it to handle a variety of recording conditions sampled from monthsof previously collected data as well as synthetic training data perturbations.We developed a new multiplicative recurrent neural network BMI decoder thatsuccessfully learned a large variety of neural-to- kinematic mappings andbecame more robust with larger training datasets. When tested with a non-humanprimate preclinical BMI model, this decoder was robust under conditions thatdisabled a state-of-the-art Kalman filter based decoder. These results validatea new BMI strategy in which accumulated data history is effectively harnessed,and may facilitate reliable daily BMI use by reducing decoder retrainingdowntime.

Making brain-machine interfaces robust to future neural variability