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Elucidating the neuronal population code of sound location at the inferior colliculus of awake mice using a fast volumetric calcium imaging approach

Juan Carlos Boffi, Brice Bathellier, Hiroki Asari, Robert Prevedel

Date / Location: Monday, 11 July 2022 / S03-449
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Sound location is proposed to be encoded by neuronal population activity patterns at the mammalian inferior colliculus (IC). However, simultaneous recordings of IC population responses are lacking to capture these population dynamics and characterize the underlying neural code. Using a bespoke scanned temporal focusing two-photon microscope, we performed fast volumetric calcium imaging in awake mice, and simultaneously recorded the activity of unprecedentedly large populations of IC neurons in response to sounds delivered from 13 different frontal horizontal locations (azimuths). The responses of IC neurons showed marked trial-to-trial variability. Nevertheless, our information theoretic analysis identified that individual IC neurons carried a small but significant amount of information about stimulus azimuth, suggesting that the azimuth information is distributed over IC populations. To better understand a potential population code, we compared the performance of theoretical decoding models on the simultaneously recorded IC population responses. We found that a decoding model based on an ensemble of binary classifiers outperformed previously proposed single-architecture models when operating on simultaneously recorded single-trial responses. These findings support the existence of IC activity patterns that encode sound location on the single-trial basis. Our simultaneous recordings showed significant noise correlations, but they did not impact the decoder accuracy. Finally, our volumetric imaging approach revealed the presence of azimuth tuned neurons in a previously unexplored anatomical subdivision of IC: the dorsal cortex. Altogether these findings point out a role of IC as a key relay of the mammalian auditory pathway in sound source localization via parallel distributed processing.