Sensory thresholds from theory to application in screening and medical devices

本研究拟探讨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启示。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被列为重中之重。这里提出的研究探索了我们感知环境微小变化能力的关键原则。该提案旨在根据周围神经活动以及引发可检测变化所需的活动的假设，开发一个阈值现象的综合模型。与此同时，我们希望使用阈值模型在筛选和评估方面开发潜在的未来应用。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逆转包括身体和认知能力下降在内的许多问题。使用神经模型来分析听力的时间分辨率，这可能会导致潜在的新测试，可以更有效地筛查在嘈杂背景下无法听到讲话的人。机器学习技术可用于分析视觉筛查模式，从而有可能找到更有效的方法来跟踪青光眼的进展。最后，在感官衰退不可避免的情况下，本研究方案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植入后受试者所经历的视觉类型的指示。这部分研究的目标是开发最早的视觉系统模型之一，可以帮助准确预测人们在使用这种植入物时看到的东西，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".