Title: Deep neural network model of sound localization replicates “what” and “where” representations in auditory cortex
Primary area: applications to neuroscience & cognitive science
Keywords: NeuroAI, Auditory Cortex, Sound Localization, Computational Neuroscience, Dual Pathway

Abstract: Unlike visual cortex, whether the auditory cortex has parallel pathways for sound identification (“what”) and localization (“where”) is debated. It also lacks a topographic map of auditory space, like the retinotopy in visual cortex. Here, we built a deep neural network to model auditory “what” and “where” representations. We trained our model for localization only, using two-channel audio waveforms from six sound types presented from 394 locations at three sound levels. Surprisingly, the model learned six well-separated clusters by sound type, but not by sound level, in the middle layer. In the model’s last layer, sounds were further organized by spectrogram similarity: harmonic types clustered together, single-band types formed a separate group, and broadband noise lay apart from the single-band group. Sound-location representations were random in the first layer but gradually organized into patches, and occasionally into a map, in the last layer. However, formation of a spatial map did not improve localization performance. Together, our model suggests that the auditory cortex does not need to dissociate “what” and “where” or create a space map.

TL;DR: Auditory “what” and “where” in AI