A learning-based approach to artificial sensory feedback leads to optimal integration.

A learning-based approach to artificial sensory feedback leads to optimal integration.
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DOI:
10.1038/nn.3883
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发表时间:
2015-01
影响因子:
25
通讯作者:
Sabes, Philip N.
Sabes, Philip N.
中科院分区:
医学1区
文献类型:
--
作者:
Dadarlat, Maria C.;O'Doherty, Joseph E.;Sabes, Philip N.

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本体感觉-身体在空间中的位置感-在自然运动规划和执行中起着重要作用,并且对于成功的运动假体和脑机接口(BMI)也是必要的。在这里,我们证明了猴子可以学会使用最初不熟悉的多通道皮质内微刺激(ICMS)信号,该信号提供了关于手相对于看不见的目标的位置的连续信息,以完成准确的到达。此外,猴子将这种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,这表明使用BMI恢复患者本体感觉的新策略以及用于研究感觉整合的自适应机制的强大新工具。
Proprioception—the sense of the body’s position in space—plays an important role in natural movement planning and execution and will likewise be necessary for successful motor prostheses and Brain–Machine Interfaces (BMIs). Here, we demonstrated that monkeys could learn to use an initially unfamiliar multi–channel intracortical microstimulation (ICMS) 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.
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