Sensory mechanisms of manual dexterity and their application to neuroprosthetics

项目总结 手动行为需要来自手的感觉信号，包括触觉和本体感觉，这一点从 由于躯体感觉去传入而导致的严重缺陷。手的感觉成分的三个方面 人们对感觉功能知之甚少。首先，触摸的神经基础几乎完全被用来研究 被动地传递到皮肤的刺激，排除了对触觉信号是如何由和调制的任何理解 与电机命令交互。第二，本体感觉信号携带的信息不仅仅是关于时变的 手的构形，也是关于手动施加的力，但本体感对力的表示 人们对此了解甚少。第三，立体认知--对物体的三维形状的感觉 来自手的感觉信号-暗示触觉和本体感觉信号的整合，这是一个过程 人们对此知之甚少。主动触摸、手本体感觉和立体认知的研究受到了阻碍。 受到技术障碍的影响。事实上，描述自我产生的与物体的接触一直是困难的或 这是不可能的，就像跟踪足够精确的手部动作一样。为了克服这些障碍，我的团队 已经开发出一种仪器，可以让我们测量接触事件--用传感器片覆盖物体的 使用基于深度学习的计算机视觉，通过 前所未有的精确度，因为动物与物体互动。然后，我们对每个阶段的反应进行表征 沿着躯体感觉神经轴，从外周神经到皮质。这一新颖的实验装置将 允许我们研究躯体感觉的神经基础--特别是当它与手动灵活性有关时-在 生态上有效的条件。 在一个相关的研究中，我们利用我们所学到的感官处理来恢复触觉 仿生手。简而言之，我们开发了算法来将仿生手上的传感器的输出转换为 电刺激周围神经(对于截肢者)或躯体感觉皮质(对于有 四肢瘫痪)以唤起有意义的触觉知觉。我是仿生学方法的主要设计者之一 到人工触摸，它假设模拟自然神经信号的编码算法将产生更多 直观的触觉感知，从而赋予仿生手更大的灵巧性。我们在人工触摸方面的工作 包括三个部分：电刺激知觉相关性的评估，发展 感官编码算法，以及评估人工触摸对手动行为的好处。相互影响 基本的科学成果和神经工程的努力将为大脑带来更自然的人工触摸-- 受控的仿生手。

PROJECT SUMMARY Manual behavior requires sensory signals from the hand, both tactile and proprioceptive, as evidenced by the severe deficits that result from somatosensory deafferentation. Three aspects of the sensory component of hand sensory function are poorly understood. First, the neural basis of touch has been studied almost exclusively with stimuli delivered passively to the skin, precluding any understanding of how tactile signals are modulated by and interact with motor commands. Second, proprioceptive signals carry information not only about the time-varying conformation of the hand, but also about manually applied forces, but proprioceptive representations of force are poorly understood. Third, stereognosis – the sense of the three-dimensional shape of objects acquired from sensory signals arising from the hand – implies the integration of tactile and proprioceptive signals, a process about which little is known. The study of active touch, hand proprioception, and stereognosis has been hindered by technical obstacles. Indeed, characterizing self-generated contact with objects has been difficult or impossible, as has tracking hand movements with sufficient precision. To overcome these obstacles, my team has developed an apparatus that allows us to measure contact events – with a sensor sheet covering the object’s surface – and track time-varying hand postures – using deep learning-based computer vision – with unprecedented precision as animals interact with objects. We then characterize the responses at every stage along the somatosensory neuraxis, from peripheral nerve through cortex. This novel experimental set up will allow us to study the neural basis of somatosensation – particularly as it relates to manual dexterity – under ecologically valid conditions. In a related line of inquiry, we leverage what we learn about sensory processing to restore the sense of touch to bionic hands. In brief, we develop algorithms to convert the output of sensors on the bionic hand into patterns of electrical stimulation of the peripheral nerve (for amputees) or of somatosensory cortex (for people with tetraplegia) to evoke meaningful tactile percepts. I am one of the principal architects of the biomimetic approach to artificial touch, which posits that encoding algorithms that mimic natural neural signals will give rise to more intuitive tactile percepts, thereby endowing bionic hands with greater dexterity. Our work on artificial touch comprises three components: evaluation of the perceptual correlates of electrical stimulation, development of sensory encoding algorithms, and assessment of the benefits of artificial touch to manual behavior. The interplay of the basic scientific results and neural engineering efforts will result in more naturalistic artificial touch for brain- controlled bionic hands.