Topic: auditory cortical neurons

In almost every natural environment, sounds are reflected by nearby objects, producing many delayed and distorted copies of the original sound, known as reverberation. Our brains usually cope well with reverberation, allowing us to recognize sound sources regardless of their environments. In contrast, reverberation can cause severe difficulties for speech recognition algorithms and people with hearing impairments. This study examines how the auditory system copes with reverberation. We trained a linear model to recover a rich set of natural, anechoic sounds from their simulated reverberant cou

Auditory categorization is a crucial aspect of everyday communication, but we lack a clear understanding of the computations performed by the brain to define boundaries between groups of natural sounds. While modern machine-learning-based classifiers perform as well as humans in categorizing sounds, they can rely on idiosyncratic acoustic cues that diverge from biologically relevant cues. We hypothesized that, since auditory cortical neurons show robust category selectivity, training a classifier with an auditory cortical front end should more accurately reflect biological categorization. To t