Discharge patterns in the lateral superior olive of decerebrate cats

Ketamine-xylazine anesthesia depth affects auditory neuronal responses in the lateral superior olive complex of the gerbil

Studies of in vivo neuronal responses to auditory inputs in the superior olive complex (SOC) are usually done under anesthesia. However, little attention has been paid to the effect of anesthesia itself on response properties. Here, we assessed the effect of anesthesia depth under ketamine-xylazine anesthetics on auditory evoked response properties of lateral SOC neurons. Anesthesia depth was tracked by monitoring EEG spectral peak frequencies. An increase in anesthesia depth led to a decrease of spontaneous discharge activities and an elevated response threshold. The temporal responses to suprathreshold tones were also affected, with adapted responses reduced but peak responses unaffected. Deepening the anesthesia depth also increased first spike latency. However, spike jitter was not affected. Auditory brainstem responses to clicks confirmed that ketamine-xylazine anesthesia depth affects auditory neuronal activities and the effect on spike rate and spike timing persists through the auditory pathway. We concluded from those observations that ketamine-xylazine affects lateral SOC response properties depending on the anesthesia depth.NEW & NOTEWORTHY We studied how the depth of ketamine-xylazine anesthesia altered response properties of lateral superior olive complex neurons, and auditory brainstem evoked responses. Our results provide direct evidence that anesthesia depth affects auditory neuronal responses and reinforce the notion that both the anesthetics and the anesthesia depth should be considered when interpreting/comparing in vivo neuronal recordings.

Principal cells of the brainstem's interaural sound level detector are temporal differentiators rather than integrators

The brainstem’s lateral superior olive (LSO) is thought to be crucial for localizing high-frequency sounds by coding interaural sound level differences (ILD). Its neurons weigh contralateral inhibition against ipsilateral excitation, making their firing rate a function of the azimuthal position of a sound source. Since the very first in vivo recordings, LSO principal neurons have been reported to give sustained and temporally integrating ‘chopper’ responses to sustained sounds. Neurons with transient responses were observed but largely ignored and even considered a sign of pathology. Using the Mongolian gerbil as a model system, we have obtained the first in vivo patch clamp recordings from labeled LSO neurons and find that principal LSO neurons, the most numerous projection neurons of this nucleus, only respond at sound onset and show fast membrane features suggesting an importance for timing. These results provide a new framework to interpret previously puzzling features of this circuit.

Physiological models of the lateral superior olive

In computational biology, modeling is a fundamental tool for formulating, analyzing and predicting complex phenomena. Most neuron models, however, are designed to reproduce certain small sets of empirical data. Hence their outcome is usually not compatible or comparable with other models or datasets, making it unclear how widely applicable such models are. In this study, we investigate these aspects of modeling, namely credibility and generalizability, with a specific focus on auditory neurons involved in the localization of sound sources. The primary cues for binaural sound localization are comprised of interaural time and level differences (ITD/ILD), which are the timing and intensity differences of the sound waves arriving at the two ears. The lateral superior olive (LSO) in the auditory brainstem is one of the locations where such acoustic information is first computed. An LSO neuron receives temporally structured excitatory and inhibitory synaptic inputs that are driven by ipsi- and contralateral sound stimuli, respectively, and changes its spike rate according to binaural acoustic differences. Here we examine seven contemporary models of LSO neurons with different levels of biophysical complexity, from predominantly functional ones (‘shot-noise’ models) to those with more detailed physiological components (variations of integrate-and-fire and Hodgkin-Huxley-type). These models, calibrated to reproduce known monaural and binaural characteristics of LSO, generate largely similar results to each other in simulating ITD and ILD coding. Our comparisons of physiological detail, computational efficiency, predictive performances, and further expandability of the models demonstrate (1) that the simplistic, functional LSO models are suitable for applications where low computational costs and mathematical transparency are needed, (2) that more complex models with detailed membrane potential dynamics are necessary for simulation studies where sub-neuronal nonlinear processes play important roles, and (3) that, for general purposes, intermediate models might be a reasonable compromise between simplicity and biological plausibility.

Computational models help our understanding of complex biological systems, by identifying their key elements and revealing their operational principles. Close comparisons between model predictions and empirical observations ensure our confidence in a model as a building block for further applications. Most current neuronal models, however, are constructed to replicate only a small specific set of experimental data. Thus, it is usually unclear how these models can be generalized to different datasets and how they compare with each other. In this paper, seven neuronal models are examined that are designed to reproduce known physiological characteristics of auditory neurons involved in the detection of sound source location. Despite their different levels of complexity, the models generate largely similar results when their parameters are tuned with common criteria. Comparisons show that simple models are computationally more efficient and theoretically transparent, and therefore suitable for rigorous mathematical analyses and engineering applications including real-time simulations. In contrast, complex models are necessary for investigating the relationship between underlying biophysical processes and sub- and suprathreshold spiking properties, although they have a large number of unconstrained, unverified parameters. Having identified their advantages and drawbacks, these auditory neuron models may readily be used for future studies and applications.

Roles for Coincidence Detection in Coding Amplitude-Modulated Sounds

Many sensory neurons encode temporal information by detecting coincident arrivals of synaptic inputs. In the mammalian auditory brainstem, binaural neurons of the medial superior olive (MSO) are known to act as coincidence detectors, whereas in the lateral superior olive (LSO) roles of coincidence detection have remained unclear. LSO neurons receive excitatory and inhibitory inputs driven by ipsilateral and contralateral acoustic stimuli, respectively, and vary their output spike rates according to interaural level differences. In addition, LSO neurons are also sensitive to binaural phase differences of low-frequency tones and envelopes of amplitude-modulated (AM) sounds. Previous physiological recordings in vivo found considerable variations in monaural AM-tuning across neurons. To investigate the underlying mechanisms of the observed temporal tuning properties of LSO and their sources of variability, we used a simple coincidence counting model and examined how specific parameters of coincidence detection affect monaural and binaural AM coding. Spike rates and phase-locking of evoked excitatory and spontaneous inhibitory inputs had only minor effects on LSO output to monaural AM inputs. In contrast, the coincidence threshold of the model neuron affected both the overall spike rates and the half-peak positions of the AM-tuning curve, whereas the width of the coincidence window merely influenced the output spike rates. The duration of the refractory period affected only the low-frequency portion of the monaural AM-tuning curve. Unlike monaural AM coding, temporal factors, such as the coincidence window and the effective duration of inhibition, played a major role in determining the trough positions of simulated binaural phase-response curves. In addition, empirically-observed level-dependence of binaural phase-coding was reproduced in the framework of our minimalistic coincidence counting model. These modeling results suggest that coincidence detection of excitatory and inhibitory synaptic inputs is essential for LSO neurons to encode both monaural and binaural AM sounds.

Detecting coincident arrivals of synaptic inputs is a shared fundamental property of many sensory neurons. Such 'coincidence detection' usually refers to the detection of synchronized excitatory inputs only. Experimental evidence, however, indicated that some auditory neurons are also sensitive to the relative timing of excitatory and inhibitory synaptic inputs. This type of sensitivity is suggested to be important for coding temporal information of amplitude-modulated sounds, such as speech and other naturalistic sounds. In this study, we used a minimal model of coincidence detection to identify the key elements for temporal information processing. Our series of simulations demonstrated that (1) the threshold and time window for coincidence detection were the major factors for determining the response properties to excitatory inputs, and that (2) timed interactions between excitatory and inhibitory synaptic inputs are responsible for determining the temporal tuning properties of the neuron. These results suggest that coincidence detection is an essential function of neurons that detect the 'anti-coincidence' of excitatory and inhibitory inputs to encode temporal information of sounds.

Subthreshold resonance properties contribute to the efficient coding of auditory spatial cues

Neurons in the medial superior olive (MSO) and lateral superior olive (LSO) of the auditory brainstem code for sound-source location in the horizontal plane, extracting interaural time differences (ITDs) from the stimulus fine structure and interaural level differences (ILDs) from the stimulus envelope. Here, we demonstrate a postsynaptic gradient in temporal processing properties across the presumed tonotopic axis; neurons in the MSO and the low-frequency limb of the LSO exhibit fast intrinsic electrical resonances and low input impedances, consistent with their processing of ITDs in the temporal fine structure. Neurons in the high-frequency limb of the LSO show low-pass electrical properties, indicating they are better suited to extracting information from the slower, modulated envelopes of sounds. Using a modeling approach, we assess ITD and ILD sensitivity of the neural filters to natural sounds, demonstrating that the transformation in temporal processing along the tonotopic axis contributes to efficient extraction of auditory spatial cues.