COCHLEAR IMPLANT STIMULATION OPTIMIZATION

这个子项目是许多研究子项目中利用 资源由NIH/NCRR资助的中心拨款提供。子项目和 调查员(PI)可能从NIH的另一个来源获得了主要资金， 并因此可以在其他清晰的条目中表示。列出的机构是 该中心不一定是调查人员的机构。 在深度失聪的人身上植入人工耳蜗，以部分恢复听力。这些植入物对植入物麦克风测量到的听觉刺激做出反应，对耳蜗电刺激。到目前为止，用于将听觉刺激转换为施加在耳蜗长上的电刺激模式的算法非常简单，并且没有针对单词识别进行优化。我们感兴趣的是开发参数刺激算法和优化参数以最小化正常听力听者和接受人工耳蜗术的人的神经激活模式(神经放电随时间和位置的概率)之间的知觉差异。由于实用性的原因，有必要使用模拟器来计算神经激活模式。该模拟器是一种蒙特卡罗模拟，非常适合并行计算。然而，考虑到我们希望执行的优化的迭代性质，必须进行许多这样的模拟。因此，访问计算机集群将是非常有利的。

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. Cochlear implants are used in profoundly deaf individuals to partially restore the sense of hearing. These implants electrically stimulate the cochlea in response to auditory stimuli measured at the implant's microphone. To date, the algorithms used to convert the auditory stimuli to the pattern of electrical stimuli applied over the length of the cochlea have been quite simple and have not been optimized for word recognition. We are interested in developing parametric stimulation algorithms and optimizing the parameters to minimize the perceptual difference between the neural activation patterns (neural firing probability over time and position in the cochlea) of normal hearing listeners and individuals using cochlear implants. For reasons of practicality, it is necessary to use a simulator to compute the neural activation patterns. This simulator is a Monte Carlo simulation, making it well suited to parallel computing. However, given the iterative nature of the optimizations we wish to perform, many such simulations must be made. Thus, access to a computer cluster would be quite advantageous.