Neuromorphic encoding of tactile stimuli to provide naturalistic sensory feedback in upper limb prostheses

项目总结 这项研究项目的目的是通过以下方式改善截肢者的生活 触觉刺激的自然主义感觉反馈。今天，假肢依赖于通过解码用户意图 测量神经或肌电(EMG)信号。这些尖端技术的全部潜力 如果没有评估环境和环境的传感器的整合，机器人设备就无法实现 一种与用户无缝通信的方式。神经假体可以使这种无缝通信成为可能 通过与截肢者的神经系统直接对接，刺激神经，从而引发 假体与环境之间相互作用所对应的感觉。要做到这一点 自然而然地，来自集成到假体中的传感器的模拟读数必须编码到 神经系统的语言：尖峰活动的模式。我的目标是改善感官反馈 截肢者通过探索来自触觉传感器的信息如何转化为神经元样 (神经形态的)用于刺激反馈的棘波。我将研究触觉感知是如何编码的 在生物学中，然后使用计算模型现象学地重建信号处理链 这将用真实世界的纹理数据集进行测试。这些模型的输出将被分类以验证 并将纹理信息的成功编码量化为神经形态的尖峰活动。纹理 由于其丰富的时空结构，可以作为开发这些模型的很好的测试用例。 具体目标1-触觉刺激的神经形态编码和处理-I将使用Izhikevich 重建SA和RA机械感受器对质地刺激反应的神经元模型 应用于触觉传感阵列。我将开发新的算法来将尖峰模式转换为帐户 用于扫描速度和施加的力。这将导致速度和力不变的表示 纹理。 特定目标2-触觉信息的神经形态压缩-最初，一个幼稚的通道 一种利用峰值训练距离评价不同输入之间互信息的选择算法 通道将压缩触觉信息以选择要通过的最佳传感通道集 刺激。更高级的方案将把输入组合在一起，以实现更有效的信息编码 并丰富了最终输出的尖峰模式的信息内容。人工纹理分类 将用于评估这些方法有效地保留相关纹理信息的能力。 从根本上说，目标1侧重于独立于触觉刺激的稳健表示 探索性条件，而目标2侧重于这些刺激的有效表征。完工后， 这项工作将为通过周围神经向截肢者提供更自然的感觉反馈提供基础 当他们在日常生活中使用假肢时，刺激将导致更好的功能结果。

PROJECT SUMMARY This research project is motivated by the goal of improving the lives of amputees through naturalistic sensory feedback of tactile stimuli. Today, prostheses rely on decoding user intention through measurement of neural or electromyographic (EMG) signals. The full potential of these sophisticated robotic devices cannot be realized without the incorporation of sensors that evaluate the environment and a way to seamlessly communicate with the user. Neural prostheses can enable this seamless communication by interfacing directly with the nervous system of amputees and stimulating the nerves in order to elicit sensations corresponding to the interaction between the prosthesis and the environment. To do this naturalistically, the analog readings from sensors incorporated into the prosthesis must be encoded into the language of the nervous system: patterns of spiking activity. My goal is to improve sensory feedback for amputees by exploring how information from tactile sensors can be transformed into neuron-like (neuromorphic) spikes to be used for stimulation feedback. I will examine how tactile sensing is encoded in biology and then phenomenologically recreate the signal processing chain using a computational model that will be tested with a real-world texture dataset. The output of these models will be classified to verify and quantify the successful encoding of texture information as neuromorphic spiking activity. Texture serves as a good test case to develop these models because of its rich spatiotemporal structure. Specific Aim 1 – Neuromorphic Encoding and Processing of Tactile Stimuli – I will use the Izhikevich neuron model to recreate the spiking activity of SA and RA mechanoreceptors in response to texture stimuli applied to a tactile sensing array. I will develop new algorithms to transform the spiking patterns to account for scanning speed and applied force. This will result in a speed- and force-invariant representation of texture. Specific Aim 2 – Neuromorphic Compression of Tactile Information – Initially, a naïve channel selection algorithm that uses spike train distance to evaluate mutual information between different input channels will compress tactile information to select an optimal set of sensing channels to pass through to stimulation. A more advanced scheme will combine inputs together for more efficient information encoding and to enrich the information content of the final output spiking patterns. Artificial texture classification will be used to evaluate the capability of these methods to efficiently retain relevant texture information. Fundamentally, Aim 1 focuses on robust representations of tactile stimuli independent of exploratory conditions, while Aim 2 focuses on efficient representations of those stimuli. When completed, this work will provide the basis for more naturalistic sensory feedback to amputees through peripheral nerve stimulation which will result in better functional outcomes when using prostheses in their daily lives.