Topic: biologically grounded classifiers

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