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

项目概要 该研究项目的目标是通过改善截肢者的生活 触觉刺激的自然感觉反馈。如今，假肢依赖于通过以下方式解码用户意图： 神经或肌电图 (EMG) 信号的测量。这些复杂的技术的全部潜力 如果不结合评估环境和环境的传感器，机器人设备就无法实现。 一种与用户无缝沟通的方式。神经假体可以实现这种无缝通信 通过直接与截肢者的神经系统连接并刺激神经以引起 与假肢和环境之间的相互作用相对应的感觉。要做到这一点 自然地，假肢中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 通过探索如何将来自触觉传感器的信息转化为类似神经元的信息来帮助截肢者 （神经形态）尖峰用于刺激反馈。我将研究触觉传感是如何编码的 在生物学中，然后使用计算模型在现象学上重新创建信号处理链 将使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尖峰活动。纹理 由于其丰富的时空结构，它可以作为开发这些模型的良好测试用例。 具体目标 1 – 触觉刺激的神经形态编码和处理 – 我将使用 Izhikevich 神经元模型重建 SA 和 RA 机械感受器响应纹理刺激的尖峰活动 应用于触觉传感阵列。我将开发新的算法来转换尖峰模式以解释 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。 具体目标 2 – 触觉信息的神经形态压缩 – 最初是一个简单的通道 使用脉冲序列距离来评估不同输入之间的互信息的选择算法 通道将压缩触觉信息以选择一组最佳的传感通道来传递到 刺激。更先进的方案将把输入组合在一起以实现更有效的信息编码 并丰富最终输出尖峰模式的信息内容。人造纹理分类 将用于评估这些方法有效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征 探索性条件，而目标 2 侧重于这些刺激的有效表示。完成后， 这项工作将为截肢者通过周围神经提供更自然的感觉反馈奠定基础 在日常生活中使用假肢时，刺激将带来更好的功能结果。

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.