Biomimetic Somatosensory Feedback through Intracorticalmicrostimulation

脊髓损伤会导致四肢瘫痪和感觉丧失。在过去的15年里 在“脑机接口”（BMI）方面取得了显著进展， 使用直接从大脑记录的信号来模拟肢体。然而，这些运动仍然 缓慢，笨拙，努力，看起来非常像那些从他们的身体中失去知觉的人。 手臂由于周围神经病变。大脑控制的假肢不太可能实现高水平的 在没有人工感觉反馈的情况下的表现。早期恢复躯体感觉的尝试 皮质内微刺激（ICMS）激活躯体感觉皮层（s1），需要动物大量学习 刺激的任意模式来表示两个或三个虚拟对象或在二维空间中导航， 空间虽然这是一个重要的开端，但这种方法似乎不太可能扩展到肢体的广泛范围。 我们在日常生活中所经历的物体的运动和相互作用。 为了克服这一障碍，我们建议用多功能触摸代替触摸和本体感觉。 电极ICMS，以产生猴子S1神经元活动的自然模式。在目标1中，我们将开发 肢体状态（指尖上的压力或肢体的运动）与 ICMS模式需要引起S1激活，模仿自然输入。这些地图将说明 神经反应的动力学和ICMS的生物物理学。我们预计这种仿生 这种方法将唤起直觉的感觉，需要很少或不需要训练来解释。我们会验证地图 通过比较自然和ICMS诱发的S1活动，使用允许同时进行ICMS的新型硬件， 神经记录在目标2中，我们将测试猴子使用人工触摸识别物体的能力。具有 动物们学会了通过触摸来识别真实的物体，它们会用一个化身来探索虚拟物体，这个化身会在它们的阴影下。 当化身接触对象时，接收人工触摸感觉。我们将测试他们的 在没有学习的情况下，虚拟立体感任务的初始表现，以及他们在 性能随着时间的推移。目标3将是类似的，但将侧重于本体感觉。我们会训练猴子 施加在他们手上的短暂力量碰撞的方向。训练结束后，我们将用 通过图案化的ICMS创建的虚拟颠簸，再次要求猴子报告他们感知到的 扰动的方向和大小。最后，在目标4中，我们将暂时麻痹猴子的手臂， 从而消除触摸和本体感觉，模仿瘫痪的基本特征， 病人虚拟化身将根据来自运动皮层的记录进行控制，并由人工神经元引导。 躯体感觉猴子会接触到一组虚拟物体，找到一个具有特定形状的物体，抓住它， 并将其移至新位置。如果我们能证明这种模型优化的仿生反馈是 信息量大，易于学习，它应该形成强大的，可扩展的，身体感觉反馈的BMI的基础。

Spinal cord injury causes both paralysis and loss of sensation from the limbs. The past 15 years have seen remarkable advances in “Brain Machine Interfaces” (BMIs) that allow paralyzed persons to move anthropomorphic limbs using signals recorded directly from their brains. However, these movements remain slow, clumsy, and effortful, looking remarkably like those of individuals who have lost sensation from their arms due to peripheral neuropathy. Brain-controlled prosthetic limbs are unlikely to achieve high levels of performance in the absence of artificial sensory feedback. Early attempts at restoring somatosensation used intracortical microstimulation (ICMS) to activate somatosensory cortex (s1), requiring animals to learn largely arbitrary patterns of stimulation to represent two or three virtual objects or to navigate in two-dimensional space. While an important beginning, this approach seems unlikely to scale to the broad range of limb movements and interactions with objects that we experience in daily life. To move the field past this hurdle, we propose to replace both touch and proprioception by using multi- electrode ICMS to produce naturalistic patterns of neuronal activity in S1 of monkeys. In Aim 1, we will develop model-optimized mappings between limb state (pressure on the fingertip, or motion of the limb) and the patterns of ICMS required to evoke S1 activation that mimics that of natural inputs. These maps will account for both the dynamics of neural responses and the biophysics of ICMS. We anticipate that this biomimetic approach will evoke intuitive sensations that require little or no training to interpret. We will validate the maps by comparing natural and ICMS-evoked S1 activity using novel hardware that allows for concurrent ICMS and neural recording. In Aim 2, we will test the ability of monkeys to recognize objects using artificial touch. Having learned to identify real objects by touch, animals will explore virtual objects with an avatar that shadows their own hand movements, receiving artificial touch sensations when the avatar contacts objects. We will test their initial performance on the virtual stereognosis task without learning, as well as their improvements in performance over time. Aim 3 will be similar, but will focus on proprioception. We will train monkeys to report the direction of brief force bumps applied to their hand. After training, we will replace the actual bumps with virtual bumps created by patterned ICMS, again asking the monkeys to report their perceived sense of the direction and magnitude of the perturbation. Finally, in Aim 4, we will temporarily paralyze the monkey's arm, thereby removing both touch and proprioception, mimicking the essential characteristics of a paralyzed patient. The avatar will be controlled based on recordings from motor cortex and guided by artificial somatosensation. The monkey will reach to a set of virtual objects, find one with a particular shape, grasp it, and move it to a new location. If we can demonstrate that this model-optimized, biomimetic feedback is informative and easy to learn, it should form the basis for robust, scalable, somatosensory feedback for BMIs.