  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.
