Dadarlat Maria C, O'Doherty Joseph E, Sabes Philip N
1] Department of Physiology, University of California, San Francisco, California, USA. [2] Center for Integrative Neuroscience, University of California, San Francisco, California, USA. [3] UC Berkeley-UCSF Center for Neural Engineering and Prosthetics, University of California, San Francisco, California, USA. [4] UC Berkeley-UCSF Graduate Program in Bioengineering, University of California, San Francisco, California, USA.
1] Department of Physiology, University of California, San Francisco, California, USA. [2] Center for Integrative Neuroscience, University of California, San Francisco, California, USA. [3] UC Berkeley-UCSF Center for Neural Engineering and Prosthetics, University of California, San Francisco, California, USA.
Nat Neurosci. 2015 Jan;18(1):138-44. doi: 10.1038/nn.3883. Epub 2014 Nov 24.
Proprioception-the sense of the body's position in space-is important to natural movement planning and execution and will likewise be necessary for successful motor prostheses and brain-machine interfaces (BMIs). Here we demonstrate that monkeys were able to learn to use an initially unfamiliar multichannel intracortical microstimulation signal, which provided continuous information about hand position relative to an unseen target, to complete accurate reaches. Furthermore, monkeys combined this artificial signal with vision to form an optimal, minimum-variance estimate of relative hand position. These results demonstrate that a learning-based approach can be used to provide a rich artificial sensory feedback signal, suggesting a new strategy for restoring proprioception to patients using BMIs, as well as a powerful new tool for studying the adaptive mechanisms of sensory integration.
本体感觉——即身体在空间中的位置感——对于自然运动的规划和执行至关重要，对于成功的运动假肢和脑机接口（BMI）同样必不可少。在这里，我们证明猴子能够学会使用最初不熟悉的多通道皮层内微刺激信号，该信号提供了关于手部相对于一个不可见目标的位置的连续信息，以完成精确的伸手动作。此外，猴子将这个人工信号与视觉相结合，形成了相对手部位置的最佳、最小方差估计。这些结果表明，基于学习的方法可用于提供丰富的人工感觉反馈信号，这为使用BMI为患者恢复本体感觉提出了一种新策略，同时也是研究感觉整合适应机制的一种强大新工具。
Zotero 插件
