Sensory thresholds from theory to application in screening and medical devices

本研究计划涉及感觉阈值的理论基础，及其对感觉障碍筛查和感觉功能恢复装置设计的意义。随着加拿大人口的迅速老龄化，听觉和视觉等感官的健康功能排在首位。这里提出的研究探索了感官的关键原则，这些原则与我们检测环境微小变化的能力有关。该提案旨在开发一个全面的模型阈值现象的外周神经活动，以及所需的活动引起可检测的变化的假设。与此同时，我们希望使用阈值模型来开发筛选和评估方面的潜在未来应用。听力和视力的丧失会极大地影响加拿大老年人的预期寿命。快速检测和诊断可以帮助扭转许多问题，包括身体和认知能力下降。使用神经模型来分析听觉的时间分辨率，这可能会导致潜在的新测试，可以更有效地筛选在嘈杂背景中无法听到语音的情况。机器学习技术可用于分析视觉筛查模式，从而可能找到更有效的方法来跟踪青光眼的进展。最后，当感觉下降是不可避免的，这项研究建议也解决了如何更好地设计视觉假体的问题。目前的设计没有提供受试者在植入后所经历的视力类型的指示。这部分研究的目标是开发视觉系统的第一个模型之一，可以帮助准确预测使用这种植入物时人们看到的内容，即提供从“刺激到看到”的全面理解。

This research proposal concerns the theoretical basis of sensory thresholds, and its implications for the screening of sensory impairment and for device design in the restoration of sensory function. With a rapidly aging Canadian population, healthy functioning of the senses like hearing and vision ranks high in priority. The research proposed here explores the key principles that underlie the senses with respect to our ability to detect small changes in the environment. The proposal seeks to develop a comprehensive model of threshold phenomena in terms of the peripheral neural activity as well as the assumptions underlying the activity required to elicit a detectable change. Parallel to this, we would like to use the threshold model to develop potential future applications in terms of screening and assessment. Both hearing and vision loss can greatly affect life expectancy of aging Canadians. Rapid detection and diagnosis can help reverse a multitude of issues including physical and cognitive decline. Using neural models to analyze the temporal resolution of hearing, this can lead to potential new tests that can more effectively screen for the inability to hear speech in noisy backgrounds. Machine learning techniques can be used to analyze visual screening patterns leading potentially to more effective ways to track the progression of glaucoma. Finally, when sensory decline is inevitable, this research proposal also addresses the issue of how to better design visual prostheses. Current designs provide no indication of the type of vision experienced by the subject after implant. The goal of this part of the research is to develop one of the very first models of the visual system that can help predict exactly what the person sees when using such an implant, i.e. provide a comprehensive understanding from "stimulation to seeing".