COCHLEAR IMPLANT STIMULATION OPTIMIZATION

这个子项目是许多研究子项目中的一个 由NIH/NCRR资助的中心赠款提供的资源。子项目和 研究者（PI）可能从另一个NIH来源获得了主要资金， 因此可以在其他CRISP条目中表示。所列机构为 研究中心，而研究中心不一定是研究者所在的机构。 耳蜗植入物用于深度耳聋患者，以部分恢复听觉。这些植入物响应于在植入物的麦克风处测量的听觉刺激而电刺激耳蜗。迄今为止，用于将听觉刺激转换为在耳蜗长度上施加的电刺激模式的算法相当简单，并且尚未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，以最大限度地减少正常听力的听众和使用人工耳蜗的个人的神经激活模式（随时间和位置的神经放电概率）之间的感知差异。出于实用性的原因，有必要使用模拟器来计算神经激活模式。这个模拟器是一个蒙特卡罗模拟，使其非常适合并行计算。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.