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

Evaluation of Neuromorphic Spike Encoding of Sound Using Information Theory