A Bio-Inspired Model for Audio Processing.

Homeostatic Activity Dependant Structural Plasticity (HADSP) is a recently introduced technique to generate network using structural plasticity. The algorithm use only homeostatic plasticity but let emerge principles of Hebbian learning. A previous study suggested that HADSP was able to generate networks that effectively leverage the inter-relationships between correlated time series but the idea was tested only on simple benchmarks. This paper examines HADSP's performance in speech recognition, its first application on a realistic dataset. Mimicking human hearing, a single-variable recording is transformed into a multi-variable time series through audio processing. The bio-inspired HADSP algorithm then creates a reservoir computing architecture, enhancing data representation and improving performance of the reservoir. Our principal results are that using spectral representation of the audio signal greatly improves the performance of speech recognition for echo state networks (ESNs). HADSP generated architectures show improvements in performance, corroborating the algorithm capacity to generate better reservoir connectivity.