Biology subjects

Oess, T.

Publications and source records attributed to Oess, T..

4 recordsLinked to original sources

bioRxiv2026

Moving Heads, Moving Sounds: Systematic Errors in Auditory Motion Perception

The movement of moving objects seen during self-motion is incorrectly perceived as a result of errors in parsing the visual motion arising from the retina and from information about the movement of the head and eyes in space1. The perception of motion of auditory targets may be even more challenging as previous research suggests that the auditory system lacks dedicated low-level speed processing mechanisms. This study investigates how the perception of a moving auditory sound source is influenced by concurrent head movements, drawing comparisons with established visual motion perception findings and illusions, such as the Filehne Illusion. Participants were asked to move their heads at a fixed speed, either horizontally (left to right or right to left) or vertically (up to down or down to up), while indicating the perceived direction of a moving sound source by using a joystick. A physical sound source was moved silently at a constant speed in fronto-parallel 2D space using a 3x3x3m cable robot. Results showed that head movements created a perceptual bias of the perceived direction of the moving sound source in the opposite direction to the head movement. Responses were less precise during horizontal than vertical head movement, whereas overall directional accuracy did not differ reliably between movement planes. The magnitude of the directional error also depended on the direction of head movement, consistent with the different cues available for horizontal and vertical localization (binaural vs. spectral cues). These findings suggest that similarly to how visual motion perception is distorted by extraretinal signals and the movement of the eyes, so too is auditory motion perception influenced by movement of the ears, revealing errors in the integration of proprioceptive, vestibular and auditory cues that distort the perceived direction of a moving sound source.

animal behavior and cognition↗

bioRxiv2020

Binaural Signal Integration Improves Vertical Sound Source Localization

Early studies have shown that the localization of a sound source in the vertical plane can be accomplished with only a single ear, thus assumed the localization mechanism to be based on monaural cues. Such cues are induced by the pinna and consist of notches and peaks in the perceived spectrum which vary systematically with the elevation of sound sources. These processes pose several problems to the auditory system like identifying and extracting spectral cues on a neural level, as well as, distinguishing pinna induced peaks and notches from features already present in the source spectrum. Interestingly, at the stage of elevation estimate binaural information from both ears is already available and it seems plausible that the auditory system takes advantage of this information. Especially, since such a binaural integration can improve the localization performance dramatically as we demonstrate in the current study. For that, we first introduce a computational model architecture that takes advantage of binaural signal integration to localize sound sources in the median plane. Model performance is tested under different conditions which reveal that localization of monaural, as well as binaural inputs is best when the model is trained with binaural inputs. Furthermore, modeling results lead to the hypothesis that sound type specific prior information is taken into account to further improve localization quality. This deduced hypothesis about vertical sound source localization is confirmed in a behavioral experiment. Based on these results, we propose that elevation estimation of sound sources is facilitated by an early binaural signal integration and can incorporate sound type specific prior information for higher accuracy.

neuroscience↗

bioRxiv2020

Computational Investigation of Visually Guided Learningof Spatially Aligned Auditory Maps in the Colliculus

The development of spatially registered auditory maps in the external nucleus of the inferior colliculus in young owls and their maintenance in adult animals is visually guided and evolves dynamically. To investigate the underlying neural mechanisms of this process, we developed a model of stabilized neoHebbian correlative learning which is augmented by an eligibility signal and a temporal trace of activations. This 3-component learning algorithm facilitates stable, yet flexible, formation of spatially registered auditory space maps composed of conductance-based topographically organized neu- ral units. Spatially aligned maps are learned for visual and auditory input stimuli that arrive in temporal and spatial registration. The reliability of visual sensory inputs can be used to regulate the learning rate in the form of an eligibility trace. We show that by shifting visual sensory inputs at the onset of learning the topography of auditory space maps is shifted accordingly. Simulation results explain why a shift of auditory maps in mature animals is possible only if corrections are induced in small steps. We conclude that learning spatially aligned auditory maps is flexibly controlled by reliable visual sensory neurons and can be formalized by a biological plausible unsupervised learning mechanism.

neuroscience↗

bioRxiv2019

Computational principles of neural adaptation for binaural signal integration

Adaptation to statistics of sensory inputs is an essential ability of neural systems and extends their effective operational range. Having a broad operational range facilitates to react to sensory inputs of different granularities, thus is a crucial factor for survival. The computation of auditory cues for spatial localization of sound sources, particularly the interaural level difference (ILD), has long been considered as a static process. Novel findings suggest that this process of ipsi- and contra-lateral signal integration is highly adaptive and depends strongly on recent stimulus statistics. Here, adaptation aids the encoding of auditory perceptual space of various granularities. To investigate the mechanism of auditory adaptation in binaural signal integration in detail, we developed a neural model architecture for simulating functions of lateral superior olive (LSO) and medial nucleus of the trapezoid body (MNTB) composed of single compartment conductance-based neurons. Neurons in the MNTB serve as an intermediate relay population. Their signal is integrated by the LSO population on a circuit level to represent excitatory and inhibitory interactions of input signals. The circuit incorporates an adaptation mechanism operating at the synaptic level based on local inhibitory feedback signals. The models predictive power is demonstrated in various simulations replicating physiological data. Incorporating the innovative adaptation mechanism facilitates a shift in neural responses towards the most effective stimulus range based on recent stimulus history. The model demonstrates that a single LSO neuron quickly adapts to these stimulus statistics and, thus, can encode an extended range of ILDs in the ipsilateral hemisphere. Most significantly, we provide a unique measurement of the adaptation efficacy of LSO neurons. Prerequisite of normal function is an accurate interaction of inhibitory and excitatory signals, a precise encoding of time and a well-tuned local feedback circuit. We suggest that the mechanisms of temporal competitive-cooperative interaction and the local feedback mechanism jointly sensitize the circuit to enable a response shift towards contra-lateral and ipsi-lateral stimuli, respectively. Author summaryWhy are we more precise in localizing a sound after hearing it several times? Adaptation to the statistics of a stimulus plays a crucial role in this. The present article investigates the abilities of a neural adaptation mechanism for improved localization skills based on a neural network model. Adaptation to stimulus statistics is very prominent in sensory systems of animals and allows them to respond to a wide range of stimuli, thus is a crucial factor for survival. For example, humans are able to navigate under suddenly changing illumination conditions (driving a car into and out of a tunnel). This is possible by courtesy of adaptation abilities of our sensory organs and pathways. Certainly, adaptation is not confined to a single sense like vision but also affects other senses like audition. Especially the perception of sound source location. Compared to vision, the localization of a sound source in the horizontal plane is a rather complicated task since the location cannot be read out from the receptor surface but needs to be computed. This requires the underlying neural system to calculate differences of the intensity between the two ears which provide a distinct cue for the location of a sound source. Here, adaptation to this cue allows to focus on a specific part of auditory space and thereby facilitates improved localisation abilities. Based on recent findings that suggest that the intensity difference computation is a flexible process with distinct adaptation mechanisms, we developed a neural model that computes the intensity difference to two incoming sound signals. The model comprises a novel mechanism for adaptation to sound source locations and provides a means to investigate underlying neural principles of adaptation and compare their effectivenesses. We demonstrate that due this mechanism the perceptual range is extended and a finer resolution of auditory space is obtained. Results explain the neural basis for adaptation and indicate that the interplay between different adaptation mechanisms facilitate highly precise sound source localization in a wide range of locations.

neuroscience↗