在耳蜗神经网络对语音信号的刺激响应过程中,针对如何区分编码最有效率的语音信号分量问题,提出了刺激条件信息分布计算方法,研究了给定刺激条件下平均不确定性度的减小。实验结果表明:积分发放神经网络膜电位发放的刺激条件信息不仅能够从统计意义上给出平均互信息的大小,而且清晰地表明信号中各分量的编码效率,确定输入信号中对于互信息量起主要作用的事件分量范围以及内部噪声的可利用性,证实噪声强度与最大刺激条件信息量之间的非单调关系,这些研究结果为进一步探索人工耳蜗动作电位发放的解码方案提供了理论依据。
For decoding information contained in the cochlea neural networks responses to speech signals, it is interesting to address which parts of input stimuli are more efficient. In this paper, the stimulus-specific information associated with a particular stimulus will be adopted to study the decrease of average uncertainties, and its calculation method is developed. We use a leaky integrate-and-fire model to capture the responses of cochlea neurons to the input speech signal, and calculate the stimulus-specific information caused by each speech signal part. It is shown that the weighted average of stimulus-specific information over the stimulus ensembles yields the mutual information, and the stimulus-specific information is also useful in clearly indentifying the stimuli that are significantly efficient to the cochlea neural network. Moreover, the stimulus-specific information can not only determine which signal component mainly contributes to the mutual information, but also confirms the availability of internal noise in the neural networks. There is a non-monotonic relationship between the noise intensity and the maximum stimulus-specific information. These results indicate that the applicability of the integrate-and-fire neuron model for current cochlear implant decoding technology deserves to be further investigated.
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