Cooperative population coding facilitates efficient sound-source separability by adaptation to input statistics.

Our sensory environment changes constantly. Accordingly, neural systems continually adapt to the concurrent stimulus statistics to remain sensitive over a wide range of conditions. Such dynamic range adaptation (DRA) is assumed to increase both the effectiveness of the neuronal code and perceptual sensitivity. However, direct demonstrations of DRA-based efficient neuronal processing that also produces perceptual benefits are lacking. Here, we investigated the impact of DRA on spatial coding in the rodent brain and the perception of human listeners. Complex spatial stimulation with dynamically changing source locations elicited prominent DRA already on the initial spatial processing stage, the Lateral Superior Olive (LSO) of gerbils. Surprisingly, on the level of individual neurons, DRA diminished spatial tuning because of large response variability across trials. However, when considering single-trial population averages of multiple neurons, DRA enhanced the coding efficiency specifically for the concurrently most probable source locations. Intrinsic LSO population imaging of energy consumption combined with pharmacology revealed that a slow-acting LSO gain-control mechanism distributes activity across a group of neurons during DRA, thereby enhancing population coding efficiency. Strikingly, such "efficient cooperative coding" also improved neuronal source separability specifically for the locations that were most likely to occur. These location-specific enhancements in neuronal coding were paralleled by human listeners exhibiting a selective improvement in spatial resolution. We conclude that, contrary to canonical models of sensory encoding, the primary motive of early spatial processing is efficiency optimization of neural populations for enhanced source separability in the concurrent environment.

Lower interaural coherence in off-signal bands impairs binaural detection.

Differences in interaural phase configuration between a target and a masker can lead to substantial binaural unmasking. This effect is decreased for masking noises with an interaural time difference (ITD). Adding a second noise with an opposing ITD in most cases further reduces binaural unmasking. Thus far, modeling of these detection thresholds required both a mechanism for internal ITD compensation and an increased filter bandwidth. An alternative explanation for the reduction is that unmasking is impaired by the lower interaural coherence in off-frequency regions caused by the second masker [Marquardt and McAlpine (2009). J. Acoust. Soc. Am. 126(6), EL177-EL182]. Based on this hypothesis, the current work proposes a quantitative multi-channel model using monaurally derived peripheral filter bandwidths and an across-channel incoherence interference mechanism. This mechanism differs from wider filters since it has no effect when the masker coherence is constant across frequency bands. Combined with a monaural energy discrimination pathway, the model predicts the differences between a single delayed noise and two opposingly delayed noises as well as four other data sets. It helps resolve the inconsistency that simulating some data requires wide filters while others require narrow filters.

Influence of envelope fluctuation on the lateralization of interaurally delayed low-frequency stimuli.

Disregarding onset and offset effects, interaurally delaying a 500 Hz tone by 1.5 ms is identical to advancing it by 0.5 ms. When presented over headphones, humans indeed perceive such a tone lateralized toward the side of the nominal lag. Any stimulus other than a tone has more than one frequency component and is thus unambiguous. It has been shown that phase ambiguity can be resolved when increasing the stimulus bandwidth. This has mostly been attributed to the integration of information across frequencies. Additionally, interaural timing information conveyed in the stimulus envelope within a single frequency channel is a second possible cue that could help to resolve phase ambiguity. This study employs stimuli designed to differ in the amount of envelope fluctuation while retaining the same power spectral density as well as interaural differences. Any difference in lateralization must thus be a result of the difference in envelope. The results show that stimuli with strong envelope fluctuation require significantly smaller bandwidths to resolve phase ambiguity when compared to stimuli with weak envelope fluctuation. This suggests that within-channel information is an important cue used to resolve phase ambiguity.

Characterization of the decline in auditory nerve phase locking at high frequencies.

The frequency dependence of phase locking in the auditory nerve influences various auditory coding mechanisms. The decline of phase locking with increasing frequency is commonly described by a low-pass filter. This study compares fitted low-pass filter parameters with the actual rate of phase locking decline. The decline is similar across studies and only 40 dB per decade, corresponding to the asymptotic decline of a second order filter.

A hemispheric two-channel code accounts for binaural unmasking in humans.

Sound in noise is better detected or understood if target and masking sources originate from different locations. Mammalian physiology suggests that the neurocomputational process that underlies this binaural unmasking is based on two hemispheric channels that encode interaural differences in their relative neuronal activity. Here, we introduce a mathematical formulation of the two-channel model - the complex-valued correlation coefficient. We show that this formulation quantifies the amount of temporal fluctuations in interaural differences, which we suggest underlie binaural unmasking. We applied this model to an extensive library of psychoacoustic experiments, accounting for 98% of the variance across eight studies. Combining physiological plausibility with its success in explaining behavioral data, the proposed mechanism is a significant step towards a unified understanding of binaural unmasking and the encoding of interaural differences in general.

Statistics of the instantaneous interaural parameters for dichotic tones in diotic noise (N0S ψ ).

Stimuli consisting of an interaurally phase-shifted tone in diotic noise-often referred to as N 0 S ψ -are commonly used to study binaural hearing. As a consequence of mixing diotic noise with a dichotic tone, this type of stimulus contains random fluctuations in both interaural phase- and level-difference. We report the joint probability density functions of the two interaural differences as a function of amplitude and interaural phase of the tone. Furthermore, a second joint probability density function for interaural phase differences and the instantaneous cross-power is derived. The closed-form expression can be used in future studies of binaural unmasking first to obtain the interaural statistics and then study more directly the relation between those statistics and binaural tone detection.

Electrical stimulation in the cochlea: Influence of modiolar microstructures on the activation of auditory nerve fibres.

Existing computational studies of cochlear implants have demonstrated that the structural detail of threedimensional (3D) cochlear models exerts influence on the current spread within the cochlea. Nevertheless, the significance of including the microstructures inside the modiolar bone in a cochlear model is still unclear in the literature. We employed two different multi-compartment neuron models to simulate auditory nerve fibres, and compared response characteristics of the fibre population between a detailed and a simplified 3D cochlear model. Results showed that although the prediction of firing is dependent on the details of the neuron model, the responses of the fibre population to the electrical stimulus, especially the location of the initiation of action potential, varied between the detailed and the simplified models. Therefore, the inclusion of the modiolar microstructures in a cochlear model may be necessary for fully understanding the firing of auditory nerve fibres.

Comparison of Multi-Compartment Cable Models of Human Auditory Nerve Fibers.

Background: Multi-compartment cable models of auditory nerve fibers have been developed to assist in the improvement of cochlear implants. With the advancement of computational technology and the results obtained from in vivo and in vitro experiments, these models have evolved to incorporate a considerable degree of morphological and physiological details. They have also been combined with three-dimensional volume conduction models of the cochlea to simulate neural responses to electrical stimulation. However, no specific rules have been provided on choosing the appropriate cable model, and most models adopted in recent studies were chosen without a specific reason or by inheritance. Methods: Three of the most cited biophysical multi-compartment cable models of the human auditory nerve, i.e., Rattay et al. (2001b), Briaire and Frijns (2005), and Smit et al. (2010), were implemented in this study. Several properties of single fibers were compared among the three models, including threshold, conduction velocity, action potential shape, latency, refractory properties, as well as stochastic and temporal behaviors. Experimental results regarding these properties were also included as a reference for comparison. Results: For monophasic single-pulse stimulation, the ratio of anodic vs. cathodic thresholds in all models was within the experimental range despite a much larger ratio in the model by Briaire and Frijns. For biphasic pulse-train stimulation, thresholds as a function of both pulse rate and pulse duration differed between the models, but none matched the experimental observations even coarsely. Similarly, for all other properties including the conduction velocity, action potential shape, and latency, the models presented different outcomes and not all of them fell within the range observed in experiments. Conclusions: While all three models presented similar values in certain single fiber properties to those obtained in experiments, none matched all experimental observations satisfactorily. In particular, the adaptation and temporal integration behaviors were completely missing in all models. Further extensions and analyses are required to explain and simulate realistic auditory nerve fiber responses to electrical stimulation.

Electrical Stimulation in the Human Cochlea: A Computational Study Based on High-Resolution Micro-CT Scans.

Background: Many detailed features of the cochlear anatomy have not been included in existing 3D cochlear models, including the microstructures inside the modiolar bone, which in turn determines the path of auditory nerve fibers (ANFs). Method: We captured the intricate modiolar microstructures in a 3D human cochlea model reconstructed from µCT scans. A new algorithm was developed to reconstruct ANFs running through the microstructures within the model. Using the finite element method, we calculated the electrical potential as well as its first and second spatial derivatives along each ANF elicited by the cochlear implant electrodes. Simulation results of electrical potential was validated against intracochlear potential measurements. Comparison was then made with a simplified model without the microstructures within the cochlea. Results: When the stimulus was delivered from an electrode located deeper in the apex, the extent of the auditory nerve influenced by a higher electric potential grew larger; at the same time, the maximal potential value at the auditory nerve also became larger. The electric potential decayed at a faster rate toward the base of the cochlea than toward the apex. Compared to the cochlear model incorporating the modiolar microstructures, the simplified version resulted in relatively small differences in electric potential. However, in terms of the first and second derivatives of electric potential along the fibers, which are relevant for the initiation of action potentials, the two models exhibited large differences: maxima in both derivatives with the detailed model were larger by a factor of 1.5 (first derivative) and 2 (second derivative) in the exemplary fibers. More importantly, these maxima occurred at different locations, and opposite signs were found for the values of second derivatives between the two models at parts along the fibers. Hence, while one model predicts depolarization and spike initiation at a given location, the other may instead predict a hyperpolarization. Conclusions: Although a cochlear model with fewer details seems sufficient for analysing the current spread in the cochlear ducts, a detailed-segmented cochlear model is required for the reconstruction of ANF trajectories through the modiolus, as well as the prediction of firing thresholds and spike initiation sites.

Extraction of Inter-Aural Time Differences Using a Spiking Neuron Network Model of the Medial Superior Olive.

The mammalian auditory system is able to extract temporal and spectral features from sound signals at the two ears. One important cue for localization of low-frequency sound sources in the horizontal plane are inter-aural time differences (ITDs) which are first analyzed in the medial superior olive (MSO) in the brainstem. Neural recordings of ITD tuning curves at various stages along the auditory pathway suggest that ITDs in the mammalian brainstem are not represented in form of a Jeffress-type place code. An alternative is the hemispheric opponent-channel code, according to which ITDs are encoded as the difference in the responses of the MSO nuclei in the two hemispheres. In this study, we present a physiologically-plausible, spiking neuron network model of the mammalian MSO circuit and apply two different methods of extracting ITDs from arbitrary sound signals. The network model is driven by a functional model of the auditory periphery and physiological models of the cochlear nucleus and the MSO. Using a linear opponent-channel decoder, we show that the network is able to detect changes in ITD with a precision down to 10 µs and that the sensitivity of the decoder depends on the slope of the ITD-rate functions. A second approach uses an artificial neuronal network to predict ITDs directly from the spiking output of the MSO and ANF model. Using this predictor, we show that the MSO-network is able to reliably encode static and time-dependent ITDs over a large frequency range, also for complex signals like speech.