Sensory mechanisms of manual dexterity and their application to neuroprosthetics

手灵巧度的感觉机制及其在神经修复学中的应用

• 批准号: 10240106
• 负责人: SLIMAN BENSMAIA
• 金额: $ 109.46万
• 依托单位: UNIVERSITY OF CHICAGO
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-05-01 至 2029-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10240106
• 关键词: 3-Dimensional; Algorithms; Amputees; Animals; Behavior; Biomimetics; Bionics; Brain; Code; Computer Vision Systems; Deafferentation procedure; Development; E-learning; Electric Stimulation; Engineering; Evaluation; Event; Hand; Intuition; Learning; Limb structure; Location; Manuals; Measures; Molecular Conformation; Monkeys; Motor; Movement; Neuraxis; Neurons; Output; Pattern; Peripheral Nerve Stimulation; Peripheral Nerves; Posture; Process; Proprioception; Quadriplegia; Sensory; Shapes; Signal Transduction; Skin; Somatosensory Cortex; Stereognosis; Stimulus; Surface; Tactile; Time; Touch sensation; Work; deep learning; dexterity; grasp; neuroprosthesis; neurotransmission; novel; object shape; relating to nervous system; response; sensor; sensory feedback; sensory mechanism; somatosensory

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.

项目摘要 手动行为需要手动信号，无论是触觉还是本体感受， 严重定义了体感应死亡的导致。手的感觉成分的三个方面 感官功能知之甚少。首先，触摸的神经元基础几乎完全研究了 刺激被动地传递到皮肤上，排除了对触觉信号如何调节和 与电动机命令互动。其次，本体感受信号不仅包含有关随时间变化的信息 手的构象，但也涉及手动施加力，但武力的本体感受表示 知之甚少。第三，立体认知 - 从中​​获得的三维形状的感觉 手动引起的感官信号 - 意味着触觉和本体感受信号的整合，一个过程 关于哪个鲜为人知。主动触摸，手部本体感受和立体认知的研究已受到阻碍 由于技术障碍。确实，表征与物体的自我生成的接触非常困难或 不可能，就像足够精确的跟踪手动运动一样。为了克服这些障碍，我的团队 已经开发了一种设备，使我们能够测量接触事件 - 传感器表覆盖了对象的 表面 - 并跟踪随时间变化的手姿势 - 使用基于深度学习的计算机视觉 - 与 当动物与物体相互作用时，空前的精度。然后，我们在每个阶段都表征回答 沿着体感神经毒素，从外周神经到皮质。这个新颖的实验设置将 允许我们研究体质的神经基础，尤其是与手动敏捷有关的神经基础 - 生态有效的条件。 在相关的询问方面，我们利用我们对感官处理学到的知识来恢复触摸感 仿生手。简而言之，我们开发了算法，以将仿生手的传感器输出转换为模式 对周围神经（适用于截肢）或体感皮质的电刺激（适用于患有 四方）唤起有意义的触觉感知。我是仿生方法的主要建筑师之一 对于人工触摸，它认为模仿天然神经信号的编码算法会产生更多 直观的触觉感知，从而使仿生手具有更大的灵巧性。我们在人造触摸方面的工作 包括三个组成部分：评估电刺激的感知相关性，发展 感官编码算法，并评估人工触摸对手动行为的好处。 在基本的科学结果和神经工程工作中，将导致更自然的人工触摸脑 - 受控的仿生手。