Available online 1 February 2024, 108966

Author links open overlay panelAbstract

The nonlinearities of the inner ear are often considered to be obstacles that the central nervous system has to overcome to decode neural responses to sounds. This review describes how peripheral nonlinearities, such as saturation of the inner-hair-cell response and of the IHC-auditory-nerve synapse, are instead beneficial to the neural encoding of complex sounds such as speech. These nonlinearities set up contrast in the depth of neural-fluctuations in auditory-nerve responses along the tonotopic axis, referred to here as neural fluctuation contrast (NFC). Physiological support for the NFC coding hypothesis is reviewed, and predictions of several psychophysical phenomena, including masked detection and speech intelligibility, are presented. Lastly, a framework based on the NFC code for understanding how the medial olivocochlear (MOC) efferent system contributes to the coding of complex sounds is presented. By modulating cochlear gain control in response to both sound energy and fluctuations in neural responses, the MOC system is hypothesized to function not as a simple feedback gain-control device, but rather as a mechanism for enhancing NFC along the tonotopic axis, enabling robust encoding of complex sounds across a wide range of sound levels and in the presence of background noise. Effects of sensorineural hearing loss on the NFC code and on the MOC feedback system are presented and discussed.

Section snippetsA Code based on Neural-Fluctuation Contrast

This review describes a theory for the neural coding and decoding of complex sounds based on the contrast in depth along the tonotopic axis of low-frequency fluctuations in auditory-nerve (AN) responses, referred to as neural-fluctuation contrast (NFC). A defining aspect of any nonlinear system is that it responds differently to inputs with different amplitudes. For the auditory system, which is often tasked with encoding spectral peaks, this feature of nonlinear systems is arguably beneficial.

Effects of Sensorineural Hearing Loss on Neural-Fluctuation Contrast

An important aspect of the NFC model is that sensorineural hearing loss (SNHL) affects the NFC in a manner that is consistent with changes observed in psychophysical performance (see below). Figure 2 illustrates responses of AN models with sensorineural hearing loss (Fig. 2A), implemented as a combined reduction in cochlear gain, accounting for 2/3 of the loss, and in reduced IHC sensitivity, accounting for 1/3 of the loss (Zilany and Bruce, 2007). Mild SNHL has a relatively small impact on NFC

Classical Theories for Neural Coding

The pure tone is the most commonly used stimulus in auditory science (and in the clinic), but the shaping of the envelopes of AN responses by peripheral nonlinearities is relatively unimportant for pure tones, which clearly capture IHC and AN responses. Saturation of the IHC-AN synapse, however, limits the dynamic range of AN fiber discharge rates. This nonlinearity has received considerable attention because it significantly limits coding schemes of sound level that are based on average

Physiological Support for the NFC Code

As mentioned above, NFC has not been a traditional focus of physiological studies of AN fibers (but see Li and Joris, 2021; Heeringa and Köppl, 2022). Nevertheless, changes in NFC along the tonotopic axis are evident in the classic studies of AN responses to vowels and harmonic complexes. For example, an illustration of the dominant components that governed the temporal responses of AN fibers (Fig. 7 in Delgutte and Kiang, 1984) shows that synchrony to F0 in response to vowels changes as a

Psychophysical Modeling Support for the NFC Code

Extensive support for the NFC model is provided by predictions of psychophysical performance, which have taken advantage of a large literature describing performance of listeners on a diverse set of tasks. To make such predictions, a quantity, referred to as a decision variable (DV), is computed based on each stimulus waveform or on the model response to each waveform. For a multiple-interval task, the target interval is selected based on a comparison of the DVs computed for each interval using

Efferent Control of Cochlear Gain and the NFC model

The NFC cues along the tonotopic axis that were explored in the physiological and psychophysical studies described above are shaped by peripheral nonlinearities and their interactions (Fig. 1). The amplitude of the cochlear response is determined by nonlinear cochlear gain at a given place in the cochlea, and the cochlear response determines the extent to which IHCs are saturated, or captured, by stimulus components near CF (Zilany and Bruce, 2007). The amplitude and time course of the IHC

Summary and Future Directions

This review introduced the NFC code for complex sounds. Effects of SNHL on NFC were also illustrated. The implications of these effects are of interest for a better understanding of how even mild hearing loss can interfere with this neural code, possibly explaining the difficulties of these listeners in understanding complex sounds, especially in noise. NFC theory provides a new framework for addressing SNHL. Amplification per se will not fully correct NFC cues; instead, stimulus spectra and

Uncited References

Osses Vecchi and Kohlrausch, 2021

CRediT authorship contribution statement

Laurel H. Carney: Conceptualization, Funding acquisition, Software, Writing – original draft, Writing – review & editing.

Acknowledgements

Funding: This work was supported by the National Institutes of Health grants R01DC001641 and R01DC010813, and by a Fellowship from the Hanse-Wissenschaftskolleg in Delmenhorst, Germany. The manuscript benefited from thoughtful comments from Emanuela Assenza, Daniel Guest, Swapna Agarwalla, David Cameron, Elizabeth Strickland, and Afagh Farhadi, as well as from Brian Moore and an anonymous reviewer. MATLAB code used for simulations is available at https://osf.io/6bsnt/ and //urhear.urmc.rochester.edu/webapps/home/

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