Zai Anja T, Bhargava Saurabh, Mesgarani Nima, Liu Shih-Chii
Institute of Neuroinformatics, University of Zurich and ETH Zurich Zurich, Switzerland.
Department of Electrical Engineering, Columbia University New York, NY, USA.
Front Neurosci. 2015 Oct 13;9:347. doi: 10.3389/fnins.2015.00347. eCollection 2015.
Spiking cochlea models describe the analog processing and spike generation process within the biological cochlea. Reconstructing the audio input from the artificial cochlea spikes is therefore useful for understanding the fidelity of the information preserved in the spikes. The reconstruction process is challenging particularly for spikes from the mixed signal (analog/digital) integrated circuit (IC) cochleas because of multiple non-linearities in the model and the additional variance caused by random transistor mismatch. This work proposes an offline method for reconstructing the audio input from spike responses of both a particular spike-based hardware model called the AEREAR2 cochlea and an equivalent software cochlea model. This method was previously used to reconstruct the auditory stimulus based on the peri-stimulus histogram of spike responses recorded in the ferret auditory cortex. The reconstructed audio from the hardware cochlea is evaluated against an analogous software model using objective measures of speech quality and intelligibility; and further tested in a word recognition task. The reconstructed audio under low signal-to-noise (SNR) conditions (SNR < -5 dB) gives a better classification performance than the original SNR input in this word recognition task.
脉冲发放式耳蜗模型描述了生物耳蜗内的模拟处理和脉冲发放过程。因此，从人工耳蜗脉冲中重建音频输入对于理解脉冲中保留的信息保真度很有用。重建过程具有挑战性，特别是对于来自混合信号（模拟/数字）集成电路（IC）耳蜗的脉冲，这是因为模型中存在多种非线性以及随机晶体管失配导致的额外方差。这项工作提出了一种离线方法，用于从一种名为AEREAR2耳蜗的特定基于脉冲的硬件模型以及等效的软件耳蜗模型的脉冲响应中重建音频输入。该方法先前曾用于根据雪貂听觉皮层中记录的脉冲响应的刺激周围直方图来重建听觉刺激。使用语音质量和可懂度的客观指标，将从硬件耳蜗重建的音频与类似的软件模型进行评估；并在单词识别任务中进行进一步测试。在该单词识别任务中，低信噪比（SNR）条件（SNR < -5 dB）下重建的音频比原始SNR输入具有更好的分类性能。
