Separation of Snr Via Dimension Expansion in a Model of the Central Auditory System

In this study, we provide a theoretical approach for analyzing signal and noise separation and the noise-robustness of class-dependent activation areas in a model of the primary auditory cortex in the central auditory system. Specifically, we interpret the auditory model as a system of localized matched filters that act as a place-coding mechanism for mapping signal and noise spectra into separate regions in the three-dimensional cortical space. This framework allows us to analyse the noise robustness of signal-respondent neurons by computing their signal-to-noise ratio (SNR)´s without having to explicitly consider the complex mathematical expressions for the auditory model. The framework is also fundamentally consistent with the notion of category-dependence proposed in our previous work. Our theoretical developments of the place-coding effect and the separation of SNR is also demonstrated experimentally