Neural computations as multidimensional feature mapping for acoustic information representation

Abstract

Neurons in biological systems usually exhibit distinctive response selectivity to certain features in the stimulus. As the neurons are functionally and spatially segregated, one may interpret the computational principles of the neural systems as a mechanism of feature mapping, which represents information in a topographic fashion. In this article, the author summarizes the physiological findings of the neural selectivities in the primary auditory cortex and, based on which, proposes a mathematical framework for mapping the acoustic features conveyed in the power spectrum. The author further demonstrates how this model may be employed to explain a series of psychoacoustic experiments that are designed to measure the sensitivity of the human auditory system to spectral shape perception, and hypothesizes how the measured thresholds may be related to the model parameters.<>