The large power requirement of current brain–machine interfaces is a major hindrance to their clinical translation. In basic behavioural tasks, the downsampled magnitude of the 300–1,000 Hz band of spiking activity can predict movement similarly to the threshold crossing rate (TCR) at 30 kilo-samples per second. However, the relationship between such a spiking-band power (SBP) and neural activity remains unclear, as does the capability of using the SBP to decode complicated behaviour. By using simulations of recordings of neural activity, here we show that the SBP is dominated by local single-unit spikes with spatial specificity comparable to or better than that of the TCR, and that the SBP correlates better with the firing rates of lower signal-to-noise-ratio units than the TCR. With non-human primates, in an online task involving the one-dimensional decoding of the movement of finger groups and in an offline two-dimensional cursor-control task, the SBP performed equally well or better than the TCR. The SBP may enhance the decoding performance of neural interfaces while enabling substantial cuts in power consumption.
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The main data supporting the results in this study are available within the paper and its Supplementary Information. The raw and analysed datasets generated during the study are too large to be publicly shared, yet they are available for research purposes from the corresponding authors on reasonable request.
The code used in this study is available from the corresponding author upon reasonable request.
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We thank E. Kennedy for animal and experimental support. We thank G. Rising, A. Yanovich, L. Burlingame, P. Lester, V. Dunivant, L. Durham, T. Hetrick, H. Noack, D. Renner, M. Bradley, G. Chan, K. Cornelius, C. Hunter, L. Krueger, R. Nichols, B. Pallas, C. Si, A. Skorupski, J. Xu, J. Yang, M. Risch, M. Wechsler and R. Reeder for expert surgical assistance and veterinary care. We thank B. Davis for administrative assistance. We thank W. L. Gore Inc. for donating Preclude artificial dura, used as part of some of the chronic electrode array implantation procedures, and S. Ryu for performing array implantation surgeries. This work was supported by NSF grant no. 1926576, Craig H. Neilsen Foundation project 315108, A. Alfred Taubman Medical Research Institute, NIH grant no. R01GM111293, MCubed project 1482 and NIH grant no. R21EY029452. S.R.N. was supported by NIH grant no. F31HD098804. A.K.V. was supported by fellowship from the Robotics Graduate Program at University of Michigan. M.S.W. was supported by NIH grant no. T32NS007222. E.J.W. was supported by NIH grant nos. U01NS094375 and UF1NS107659, and Office of the Director National Institutes of Health OT2OD024907. H.A., T.J., H.-S.K. and D.B. were supported by MCubed project 1482 and NIH grant no. R21EY029452. P.P.V., A.J.B., C.S.N. and J.C.K. were supported by NSF-GRFP. K.V.S. was supported in part by the following awards: NIH National Institute of Neurological Disorders and Stroke Transformative Research Award R01NS076460, NIH National Institute of Mental Health Transformative Research Award R01MH09964703, NIH Director’s Pioneer Award 8DP1HD075623, Defense Advanced Research Projects Agency (DARPA) Biological Technology Office (BTO) ‘REPAIR’ Award N66001-10-C-2010, DARPA BTO ‘NeuroFAST’ Award W911NF-14-2-0013, Simons Foundation Collaboration on the Global Brain award 543045, the Office of Naval Research W911NF-14-2-0013 and the Howard Hughes Medical Institute. P.G.P. was supported by NSF grant no. 1926576, A. Alfred Taubman Medical Research Institute and NIH grant no. R01GM111293. C.A.C. was supported by NSF grant no. 1926576, Craig H. Neilsen Foundation project 315108, NIH grant nos. R01GM111293 and R21EY029452, and MCubed project 1482.
K.V.S. is a consultant for Neuralink Corp. and is on the scientific advisory board for CTRL-Labs Inc., MIND-X Inc., Inscopix Inc. and Heal Inc. These entities did not provide support for this work.
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Nason, S.R., Vaskov, A.K., Willsey, M.S. et al. A low-power band of neuronal spiking activity dominated by local single units improves the performance of brain–machine interfaces. Nat Biomed Eng 4, 973–983 (2020). https://doi.org/10.1038/s41551-020-0591-0
Nature Biomedical Engineering (2020)