Neural Coding of Sound with Cochlear Damage

Compression and amplification algorithms in hearing aids impair the selectivity of neural responses to speech

In quiet environments, hearing aids improve the perception of low-intensity sounds. However, for high-intensity sounds in background noise, the aids often fail to provide a benefit to the wearer. Here, by using large-scale single-neuron recordings from hearing-impaired gerbils — an established animal model of human hearing — we show that hearing aids restore the sensitivity of neural responses to speech, but not their selectivity. Rather than reflecting a deficit in supra-threshold auditory processing, the low selectivity is a consequence of hearing-aid compression (which decreases the spectral and temporal contrasts of incoming sound) and of amplification (which distorts neural responses, regardless of whether hearing is impaired). Processing strategies that avoid the trade-off between neural sensitivity and selectivity should improve the performance of hearing aids.

Neural Spike-Train Analyses of the Speech-Based Envelope Power Spectrum Model

Diagnosing and treating hearing impairment is challenging because people with similar degrees of sensorineural hearing loss (SNHL) often have different speech-recognition abilities. The speech-based envelope power spectrum model (sEPSM) has demonstrated that the signal-to-noise ratio (SNR_ENV) from a modulation filter bank provides a robust speech-intelligibility measure across a wider range of degraded conditions than many long-standing models. In the sEPSM, noise (N) is assumed to: (a) reduce S + N envelope power by filling in dips within clean speech (S) and (b) introduce an envelope noise floor from intrinsic fluctuations in the noise itself. While the promise of SNR_ENV has been demonstrated for normal-hearing listeners, it has not been thoroughly extended to hearing-impaired listeners because of limited physiological knowledge of how SNHL affects speech-in-noise envelope coding relative to noise alone. Here, envelope coding to speech-in-noise stimuli was quantified from auditory-nerve model spike trains using shuffled correlograms, which were analyzed in the modulation-frequency domain to compute modulation-band estimates of neural SNR_ENV. Preliminary spike-train analyses show strong similarities to the sEPSM, demonstrating feasibility of neural SNR_ENV computations. Results suggest that individual differences can occur based on differential degrees of outer- and inner-hair-cell dysfunction in listeners currently diagnosed into the single audiological SNHL category. The predicted acoustic-SNR dependence in individual differences suggests that the SNR-dependent rate of susceptibility could be an important metric in diagnosing individual differences. Future measurements of the neural SNR_ENV in animal studies with various forms of SNHL will provide valuable insight for understanding individual differences in speech-in-noise intelligibility.

Basic response properties of auditory nerve fibers: a review

All acoustic information from the periphery is encoded in the timing and rates of spikes in the population of spiral ganglion neurons projecting to the central auditory system. Considerable progress has been made in characterizing the physiological properties of type-I and type-II primary auditory afferents and understanding the basic properties of type-I afferents in response to sounds. Here, we review some of these properties, with emphasis placed on issues such as the stochastic nature of spike timing during spontaneous and driven activity, frequency tuning curves, spike-rate-versus-level functions, dynamic-range and spike-rate adaptation, and phase locking to stimulus fine structure and temporal envelope. We also review effects of acoustic trauma on some of these response properties.