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.

项目概要 手动行为需要来自手的感觉信号，包括触觉信号和本体感觉信号，正如 由体感传入神经阻滞导致的严重缺陷 手部感觉成分的三个方面。 首先，人们对感觉功能知之甚少。 刺激被动地传递到皮肤，无法理解触觉信号是如何调节的 其次，本体感受信号不仅携带有关时变的信息。 手的构造，还涉及手动施加的力，以及力的本体感觉表征 第三，立体感——从物体中获得的三维形状的感觉。 来自手的感觉信号——意味着触觉和本体感觉信号的整合，一个过程 主动触觉、手部本体感觉和立体认知的研究受到了阻碍。 事实上，描述与物体的自生接触一直很困难或困难。 不可能，因为以足够的精度跟踪手部运动是不可能的，为了克服这些障碍，我的团队 开发了一种设备，使我们能够测量接触事件 - 用覆盖物体的传感器片 表面 - 并跟踪随时间变化的手势 - 使用基于深度学习的计算机视觉 - 与 然后我们描述动物与物体相互作用时的精确性。 沿着体感神经轴，从周围神经穿过皮层。 让我们能够研究体感的神经基础——特别是与手的灵活性相关的—— 生态有效条件。 在相关的探究中，我们利用我们所学到的感官处理知识来恢复触觉 简而言之，我们开发了将仿生手上传感器的输出转换为模式的算法。 周围神经（对于截肢者）或体感皮层（对于患有以下疾病的人）的电刺激 四肢瘫痪）来唤起有意义的触觉感知我是仿生方法的主要设计师之一。 人工触摸，模仿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活性。 包括三个部分：电刺激知觉相关性的评估、 感觉编码算法，以及评估人工触摸对手动行为的相互作用的好处。 基础科学成果和神经工程努力将为大脑带来更自然的人工触摸 受控仿生手。