Investigating the Response of CNS Neurons to Electric and Magnetic Stimulation

我们的长期目标是更好地了解神经元对人工刺激的反应，并利用这一点 开发新的、更有效的刺激无功能或不正常功能神经元的策略的知识 中枢神经系统。全面准确地预测神经反应的模型的发展 事实证明，种群对电刺激具有挑战性，部分原因是显著的形态 甚至在附近的细胞之间也可能存在差异，以及对这种差异是如何存在的缺乏了解 塑造每个细胞对刺激的反应。全面了解激活过程不会 只允许开发更准确地预测人口反应的模型，但也 支持制定更有效的刺激战略。例如，在视网膜中，反应 亮度增加(开单元)通常与响应亮度降低(关)的单元相邻 细胞)；两者通常不会对相同的刺激产生动作电位，因此 同时激活两者的假体创造了一种非生理性的神经活动模式。错失- 自然信号和人工信号之间的匹配限制了视网膜所能获得的视觉质量 假体和类似的限制了其他基于中枢神经系统的假体的有效性。在此，我们建议 全面研究个体细胞特性如何影响对人工刺激的反应。 我们的方法将是绘制细胞间的敏感性图，然后将生理图与细胞图进行比较 形态，包括电压门控离子通道的表达；这将使我们能够识别特定的 对响应性影响最大的细胞区域。基于我们精确度的计算模型 解剖测量可以根据生理图谱进行校准，以优化 模型；它们还将有助于明确地确定个别特征的相对敏感度。比较 同一像元类型中的多个像元将有助于进一步识别影响最强的要素 在阈值上，并在多个细胞类型、不同中枢神经系统区域和多个物种中重复这一过程 将导致对激活过程的全面理解，以及同时开发 准确预测大量神经元群体对多种不同形式的 刺激。将非人类灵长类组织纳入研究将提高我们的翻译价值 调查结果。经过验证的模型将用于研究对更先进的刺激策略的反应，例如 高速率刺激训练，在视网膜细胞类型中产生选择性激活，以及，使用 可植入微线圈的磁刺激选择性靶向皮质锥体神经元 避免从远端神经元附近通过轴突。模型将从每一套新的 实验和(全套模型)将向研究界广泛提供。

Our long-term goals are to better understand the response of neurons to artificial stimulation, and, to use this knowledge to develop new and more effective strategies for stimulating non- or improperly-functioning neurons of the CNS. The development of models that comprehensively and accurately predict the response of neural populations to electric stimulation has proven challenging, in part because of the significant morphological differences that can exist even between nearby cells, and, a lack of understanding as to how such differences shape each cell’s response to stimulation. A comprehensive understanding of the activation process would not only allow the development of models that would more accurately predict population responses but would also support the development of more effective stimulation strategies. In the retina for example, cells that respond to increases in luminance (ON cells) typically lie adjacent to cells that respond to luminance decreases (OFF cells); the two do not typically fire action potentials in response to the same stimulus and therefore, a prosthesis that activates both simultaneously creates a pattern of neural activity that is non-physiological. Mis- match between natural and artificial signals limits the quality of vision that can be obtained by a retinal prosthesis and similarly limits the effectiveness of other CNS-based prostheses as well. Here, we propose to comprehensively study how individual cellular properties each influence the response to artificial stimulation. Our approach will be to map sensitivity across a cell, and then compare physiological maps to cellular morphology, including the expression of voltage-gated ion channels; this will allow us to identify the specific cellular regions that have the strongest influence on responsivity. Computational models based on our precise anatomical measurements can be calibrated from the physiological maps to optimize the accuracy of the models; they will also help to unequivocally identify the relative sensitivity of individual features. Comparison of multiple cells within the same cell type will help to further identify the features that have the strongest influence on threshold and repeating the process across multiple cell types, different CNS regions and multiple species will lead to a comprehensive understanding of the activation process, along with the concurrent development of models that accurately predict the response of large populations of neurons to many different forms of stimulation. The inclusion of non-human primate tissue in the study will enhance the translation value of our findings. Validated models will be used to study responses to more advanced stimulating strategies, e.g. the high-rate stimulus trains that produce selective activation in ON vs. OFF cell types of the retina, and, the use of magnetic stimulation from implantable micro-coils to selectively target pyramidal neurons in the cortex while avoiding nearby passing axons from distal neurons. Models will be further enhanced from each new set of experiments and the comprehensive set (of models) will be made widely available to the research community.