Computational principles of neural adaptation for binaural signal integration

Abstract

AbstractAdaptation 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 model’s 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.