Audio Information Processing, Department of Electrical and Computer Engineering, Technical University of Munich, Munich, Germany.
Trends Hear. 2022 Jan-Dec;26:23312165221117079. doi: 10.1177/23312165221117079.
The ability of cochlear implants (CIs) to restore hearing to profoundly deaf people is based on direct electrical stimulation of the auditory nerve fibers (ANFs). Still, CI users do not achieve as good hearing outcomes as their normal-hearing peers. The development and optimization of CI stimulation strategies to reduce that gap could benefit from computational models that can predict responses evoked by different stimulation patterns, particularly temporal responses for coding of temporal fine structure information. To that end, we present the sequential biphasic leaky integrate-and-fire (S-BLIF) model for the ANF response to various pulse shapes and temporal sequences. The phenomenological S-BLIF model is adapted from the earlier BLIF model that can reproduce neurophysiological single-fiber cat ANF data from single-pulse stimulations. It was extended with elements that simulate refractoriness, facilitation, accommodation and long-term adaptation by affecting the threshold value of the model momentarily after supra- and subthreshold stimulation. Evaluation of the model demonstrated that it can reproduce neurophysiological data from single neuron recordings involving temporal phenomena related to inter-pulse interactions. Specifically, data for refractoriness, facilitation, accommodation and spike-rate adaptation can be reproduced. In addition, the model can account for effects of pulse rate on the synchrony between the pulsatile input and the spike-train output. Consequently, the model offers a versatile tool for testing new coding strategies for, e.g., temporal fine structure using pseudo-monophasic pulses, and for estimating the status of the electrode-neuron interface in the CI user's cochlea.
人工耳蜗(CI)能够为极重度聋患者恢复听力,其原理是对听神经纤维(ANF)进行直接电刺激。尽管如此,CI 用户的听力效果仍不如正常听力的人。为了缩小这一差距,可以开发和优化 CI 刺激策略,而计算模型可以预测不同刺激模式引起的反应,特别是对时间精细结构信息进行编码的时间响应,这将使该策略受益。为此,我们提出了一种用于 ANF 对各种脉冲形状和时间序列反应的序列双相泄露积分和放电(S-BLIF)模型。该现象学 S-BLIF 模型源自早期的 BLIF 模型,该模型可以复制单脉冲刺激的神经生理学猫 ANF 数据。它通过在超阈值和亚阈值刺激后暂时影响模型的阈值,增加了模拟不应期、易化、适应和长期适应的元素,从而对其进行了扩展。模型评估表明,它可以复制涉及与脉冲间相互作用相关的时间现象的单个神经元记录的神经生理学数据。具体来说,可以复制不应期、易化、适应和脉冲率适应的数据。此外,该模型还可以解释脉冲率对脉冲输入与尖峰输出之间同步性的影响。因此,该模型为测试新的编码策略提供了一种通用工具,例如使用伪单相脉冲对时间精细结构进行编码,并估计 CI 用户耳蜗中电极-神经元界面的状态。