Publications by authors named "Johannes Wetekam"

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.

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Substantial progress in the field of neuroscience has been made from anaesthetized preparations. Ketamine is one of the most used drugs in electrophysiology studies, but how ketamine affects neuronal responses is poorly understood. Here, we used in vivo electrophysiology and computational modelling to study how the auditory cortex of bats responds to vocalisations under anaesthesia and in wakefulness.

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The mammalian frontal and auditory cortices are important for vocal behavior. Here, using local-field potential recordings, we demonstrate that the timing and spatial patterns of oscillations in the fronto-auditory network of vocalizing bats (Carollia perspicillata) predict the purpose of vocalization: echolocation or communication. Transfer entropy analyses revealed predominant top-down (frontal-to-auditory cortex) information flow during spontaneous activity and pre-vocal periods.

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Identifying unexpected acoustic inputs, which allows to react appropriately to new situations, is of major importance for animals. Neural deviance detection describes a change of neural response strength to a stimulus solely caused by the stimulus' probability of occurrence. In the present study, we searched for correlates of deviance detection in auditory brainstem responses obtained in anaesthetised bats (Carollia perspicillata).

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Distress calls are a vocalization type widespread across the animal kingdom, emitted when the animals are under duress, e.g. when captured by a predator.

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An objective method to evaluate auditory brainstem-evoked responses (ABR) based on the root-mean-square (rms) amplitude of the measured signal and bootstrapping procedures was used to determine threshold curves (see Lv et al. in Med Eng Phys 29:191-198, 2007; Linnenschmidt and Wiegrebe in Hear Res 373:85-95, 2019). The rms values and their significance for threshold determination depended strongly on the filtering of the signal.

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