Neural integration of acoustic statistics enables detecting acoustic targets in noise

Sound detection amidst noise presents an important challenge in audition. Many naturally occurring sounds (rain, wind) can be described and predicted only statistically, so-called sound textures. Previous research has demonstrated the human ability to leverage this statistical predictability for sound recognition, but the neural mechanisms remain elusive. We trained mice to detect vocalizations embedded in sound textures with different statistical predictability, while recording and optogenetically modulating the neural activity in the auditory cortex. Mice showed improved performance and neural encoding if they could sample the statistics longer per trial. Textures with more exploitable structure, specifically higher cross-frequency correlations improved behavioral performance as well as neural representation of background and vocalization. Activating parvalbumin-positive (PV) interneurons had an asymmetric effect, improving detection and neural encoding of vocalizations for low correlations, and impoverishing them for high cross-frequency correlations. In summary, mice exploit stimulus statistics to improve sound detection in naturalistic background noise, reflected in behavioral performance and neural activity, relying on PV interneurons for temporal integration. HighlightsO_LIMice integrate statistical information indicated by behavior and neural activity C_LIO_LIEncoding of background sounds stays stable in A1, while vocalizations are enhanced C_LIO_LIHigh cross-frequency correlations improve target detection and neural encoding C_LIO_LIActivating PV cells improves detection of sounds with low cross-frequency correlations C_LI