Goldwyn Joshua H, Shea-Brown Eric, Rubinstein Jay T
Department of Applied Mathematics, University of Washington, Seattle, WA, USA.
J Comput Neurosci. 2010 Jun;28(3):405-24. doi: 10.1007/s10827-010-0224-9. Epub 2010 Feb 23.
Cochlear implant speech processors stimulate the auditory nerve by delivering amplitude-modulated electrical pulse trains to intracochlear electrodes. Studying how auditory nerve cells encode modulation information is of fundamental importance, therefore, to understanding cochlear implant function and improving speech perception in cochlear implant users. In this paper, we analyze simulated responses of the auditory nerve to amplitude-modulated cochlear implant stimuli using a point process model. First, we quantify the information encoded in the spike trains by testing an ideal observer's ability to detect amplitude modulation in a two-alternative forced-choice task. We vary the amount of information available to the observer to probe how spike timing and averaged firing rate encode modulation. Second, we construct a neural decoding method that predicts several qualitative trends observed in psychophysical tests of amplitude modulation detection in cochlear implant listeners. We find that modulation information is primarily available in the sequence of spike times. The performance of an ideal observer, however, is inconsistent with observed trends in psychophysical data. Using a neural decoding method that jitters spike times to degrade its temporal resolution and then computes a common measure of phase locking from spike trains of a heterogeneous population of model nerve cells, we predict the correct qualitative dependence of modulation detection thresholds on modulation frequency and stimulus level. The decoder does not predict the observed loss of modulation sensitivity at high carrier pulse rates, but this framework can be applied to future models that better represent auditory nerve responses to high carrier pulse rate stimuli. The supplemental material of this article contains the article's data in an active, re-usable format.
人工耳蜗语音处理器通过向耳蜗内电极传递调幅电脉冲序列来刺激听神经。因此，研究听神经细胞如何编码调制信息对于理解人工耳蜗功能以及改善人工耳蜗使用者的言语感知至关重要。在本文中，我们使用点过程模型分析听神经对调幅人工耳蜗刺激的模拟反应。首先，我们通过测试理想观察者在二选一强制选择任务中检测调幅的能力，来量化编码在脉冲序列中的信息。我们改变观察者可获得的信息量，以探究脉冲时间和平均发放率如何编码调制信息。其次，我们构建了一种神经解码方法，该方法预测了在人工耳蜗聆听者调幅检测心理物理学测试中观察到的几种定性趋势。我们发现调制信息主要存在于脉冲时间序列中。然而，理想观察者的表现与心理物理学数据中观察到的趋势不一致。使用一种神经解码方法，该方法对脉冲时间进行抖动以降低其时间分辨率，然后从异质模型神经细胞群体的脉冲序列中计算锁相的通用度量，我们预测了调制检测阈值对调制频率和刺激水平的正确定性依赖性。该解码器无法预测在高载波脉冲率下观察到的调制灵敏度损失，但此框架可应用于未来能更好地表示听神经对高载波脉冲率刺激反应的模型。本文的补充材料以一种活跃、可重复使用的格式包含了文章的数据。
