The problem of spike encoding of sound consists in transforming a sound waveform into spikes. It is of interest in many domains, including the development of audio-based spiking neural networks, where it is the first and most crucial stage of processing. Many algorithms have been proposed to perform spike encoding of sound. However, a systematic approach to quantitatively evaluate their performance is currently lacking. We propose the use of an information-theoretic framework to solve this problem. Specifically, we evaluate the coding efficiency of four spike encoding algorithms on two coding tasks that consist of coding the fundamental characteristics of sound: frequency and amplitude. The algorithms investigated are: Independent Spike Coding, Send-on-Delta coding, Ben's Spiker Algorithm, and Leaky Integrate-and-Fire coding. Using the tools of information theory, we estimate the information that the spikes carry on relevant aspects of an input stimulus. We find disparities in the coding efficiencies of the algorithms, where Leaky Integrate-and-Fire coding performs best. The information-theoretic analysis of their performance on these coding tasks provides insight on the encoding of richer and more complex sound stimuli.

翻译：声音脉冲编码问题旨在将声音波形转换为脉冲信号。这在许多领域具有重要意义，包括基于音频的脉冲神经网络开发——这是处理过程中最首要且最关键的阶段。目前已提出多种声音脉冲编码算法，但缺乏对其性能进行定量评估的系统性方法。我们提出采用信息论框架来解决该问题。具体而言，我们在两个编码任务（频率编码与幅度编码，即声音基本特征编码）中评估了四种脉冲编码算法的编码效率。研究的算法包括：独立脉冲编码、增量发送编码、Ben's Spiker算法以及泄漏积分点火编码。利用信息论工具，我们估算了脉冲信号承载的输入刺激相关特征信息量。研究发现各算法的编码效率存在差异，其中泄漏积分点火编码表现最优。对这些编码任务中算法性能的信息论分析，为更丰富复杂的声音刺激编码提供了深入见解。