Deviance Detection to Natural Stimuli in Population Responses of the Brainstem of Bats.

Abstract

Deviance detection describes an increase of neural response strength caused by a stimulus with a low probability of occurrence. This ubiquitous phenomenon has been reported for humans and multiple other species, from subthalamic areas to the auditory cortex. Cortical deviance detection has been well characterized by a range of studies using a variety of different stimuli, from artificial to natural, with and without a behavioral relevance. This allowed the identification of a broad variety of regularity deviations that are detected by the cortex. Moreover, subcortical deviance detection has been studied with simple stimuli that are not meaningful to the subject. Here, we aim to bridge this gap by using noninvasively recorded auditory brainstem responses (ABRs) to investigate deviance detection at population level in the lower stations of the auditory system of a highly vocal species: the bat Carollia perspicillata (of either sex). Our present approach uses behaviorally relevant vocalization stimuli that are similar to the animals' natural soundscape. We show that deviance detection in ABRs is significantly stronger for echolocation pulses than for social communication calls or artificial sounds, indicating that subthalamic deviance detection depends on the behavioral meaning of a stimulus. Additionally, complex physical sound features like frequency- and amplitude modulation affected the strength of deviance detection in the ABR. In summary, our results suggest that the brain can detect different types of deviants already in the brainstem, showing that subthalamic brain structures exhibit more advanced forms of deviance detection than previously known.