论文标题
使用信息理论评估声音编码的神经形态尖峰
Evaluation of Neuromorphic Spike Encoding of Sound Using Information Theory
论文作者
论文摘要
声音编码的尖峰问题包括将声波变成尖峰。它在许多领域中引起了人们的关注，包括开发基于音频的尖峰神经网络，它是处理的第一个也是最关键的阶段。已经提出了许多算法来执行声音的尖峰编码。但是，目前缺乏一种系统的定量评估其性能的方法。我们建议使用信息理论框架来解决此问题。具体而言，我们评估了四个尖峰编码算法的编码效率，这些算法在两个编码任务中，包括编码声音的基本特征：频率和振幅。所研究的算法是：独立的尖峰编码，发送式编码，Ben的Spiker算法和泄漏的集成和火灾编码。使用信息理论的工具，我们估算了尖峰在输入刺激的相关方面所携带的信息。我们发现算法的编码效率方面差异，其中泄漏的集成和火灾编码表现最佳。他们在这些编码任务上的性能的信息理论分析提供了对富裕和更复杂的声音刺激的编码的见解。
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.
