A low-power band of neuronal spiking activity dominated by local single units improves the performance of brain-machine interfaces.,Nature Biomedical Engineering

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A low-power band of neuronal spiking activity dominated by local single units improves the performance of brain-machine interfaces.
Nature Biomedical Engineering ( IF 28.1 ) Pub Date : 2020-07-27 , DOI: 10.1038/s41551-020-0591-0
Samuel R Nason ₁ , Alex K Vaskov ₂ , Matthew S Willsey _{1,

3} , Elissa J Welle ₁ , Hyochan An ₄ , Philip P Vu ₁ , Autumn J Bullard ₁ , Chrono S Nu ₁ , Jonathan C Kao _{5,

6} , Krishna V Shenoy _{7,

8,

9,

10,

11,

12} , Taekwang Jang _{4,

13} , Hun-Seok Kim ₄ , David Blaauw ₄ , Parag G Patil _{1,

3,

14,

15} , Cynthia A Chestek _{1,

2,

4,

15}

Affiliation

Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA.
Robotics Graduate Program, University of Michigan, Ann Arbor, MI, USA.
Department of Neurosurgery, University of Michigan Medical School, Ann Arbor, MI, USA.
Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, USA.
Department of Electrical and Computer Engineering, University of California, Los Angeles, Los Angeles, CA, USA.
Neurosciences Program, University of California, Los Angeles, Los Angeles, CA, USA.
Department of Electrical Engineering, Stanford University, Stanford, CA, USA.
Department of Bioengineering, Stanford University, Stanford, CA, USA.
Department of Neurobiology, Stanford University, Stanford, CA, USA.
The Bio-X Program, Stanford University, Stanford, CA, USA.
Wu Tsai Neuroscience Institute, Stanford University, Stanford, CA, USA.
Howard Hughes Medical Institute, Stanford University, Stanford, CA, USA.
Department of Information Technology and Electrical Engineering, ETH Zürich, Zürich, Switzerland.
Department of Neurology, University of Michigan Medical School, Ann Arbor, MI, USA.
Neuroscience Graduate Program, University of Michigan, Ann Arbor, MI, USA.

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.

中文翻译：

由局部单个单元控制的低功率神经突峰活动带改善了脑机接口的性能。

当前脑机接口的强大功能需求是其临床翻译的主要障碍。在基本的行为任务中，峰值活动的300–1,000 Hz频段的降采样幅度可以预测运动，类似于每秒30千样本的阈值穿越率（TCR）。但是，这种尖峰功率（SBP）与神经活动之间的关系仍然不清楚，使用SBP解码复杂行为的能力也不清楚。通过使用神经活动记录的模拟，我们在这里显示SBP由局部单单位峰控制，其空间特异性与TCR相当或更好，并且SBP与较低信号发射速率的相关性更好。噪声比比TCR高。对于非人类的灵长类动物，在涉及手指组运动的一维解码的联机任务中，以及在脱机的二维光标控制任务中，SBP的性能均优于或优于TCR。SBP可以增强神经接口的解码性能，同时大幅降低功耗。

更新日期：2020-07-27

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