Hidden Hearing Loss Impacts the Neural Representation of Speech in Background Noise.

Many individuals with seemingly normal hearing abilities struggle to understand speech in noisy backgrounds. To understand why this might be the case, we investigated the neural representation of speech in the auditory midbrain of gerbils with "hidden hearing loss" through noise exposure that increased hearing thresholds only temporarily. In noise-exposed animals, we observed significantly increased neural responses to speech stimuli, with a more pronounced increase at moderate than at high sound intensities.

Hidden hearing loss selectively impairs neural adaptation to loud sound environments.

Exposure to even a single episode of loud noise can damage synapses between cochlear hair cells and auditory nerve fibres, causing hidden hearing loss (HHL) that is not detected by audiometry. Here we investigate the effects of noise-induced HHL on functional hearing by measuring the ability of neurons in the auditory midbrain of mice to adapt to sound environments containing quiet and loud periods. Neurons from noise-exposed mice show less capacity for adaptation to loud environments, convey less information about sound intensity in those environments, and adaptation to the longer-term statistical structure of fluctuating sound environments is impaired.

Midbrain adaptation may set the stage for the perception of musical beat.

The ability to spontaneously feel a beat in music is a phenomenon widely believed to be unique to humans. Though beat perception involves the coordinated engagement of sensory, motor and cognitive processes in humans, the contribution of low-level auditory processing to the activation of these networks in a beat-specific manner is poorly understood. Here, we present evidence from a rodent model that midbrain preprocessing of sounds may already be shaping where the beat is ultimately felt.

Periodotopy in the gerbil inferior colliculus: local clustering rather than a gradient map.

Periodicities in sound waveforms are widespread, and shape important perceptual attributes of sound including rhythm and pitch. Previous studies have indicated that, in the inferior colliculus (IC), a key processing stage in the auditory midbrain, neurons tuned to different periodicities might be arranged along a periodotopic axis which runs approximately orthogonal to the tonotopic axis. Here we map out the topography of frequency and periodicity tuning in the IC of gerbils in unprecedented detail, using pure tones and different periodic sounds, including click trains, sinusoidally amplitude modulated (SAM) noise and iterated rippled noise.

An Overrepresentation of High Frequencies in the Mouse Inferior Colliculus Supports the Processing of Ultrasonic Vocalizations.

Mice are of paramount importance in biomedical research and their vocalizations are a subject of interest for researchers across a wide range of health-related disciplines due to their increasingly important value as a phenotyping tool in models of neural, speech and language disorders. However, the mechanisms underlying the auditory processing of vocalizations in mice are not well understood. The mouse audiogram shows a peak in sensitivity at frequencies between 15-25 kHz, but weaker sensitivity for the higher ultrasonic frequencies at which they typically vocalize.

Nonlinear transfer of signal and noise correlations in cortical networks.

Signal and noise correlations, a prominent feature of cortical activity, reflect the structure and function of networks during sensory processing. However, in addition to reflecting network properties, correlations are also shaped by intrinsic neuronal mechanisms. Here we show that spike threshold transforms correlations by creating nonlinear interactions between signal and noise inputs; even when input noise correlation is constant, spiking noise correlation varies with both the strength and correlation of signal inputs.

Independent population coding of speech with sub-millisecond precision.

To understand the strategies used by the brain to analyze complex environments, we must first characterize how the features of sensory stimuli are encoded in the spiking of neuronal populations. Characterizing a population code requires identifying the temporal precision of spiking and the extent to which spiking is correlated, both between cells and over time. In this study, we characterize the population code for speech in the gerbil inferior colliculus (IC), the hub of the auditory system where inputs from parallel brainstem pathways are integrated for transmission to the cortex.

Analysis and modelling of variability and covariability of population spike trains across multiple time scales.

As multi-electrode and imaging technology begin to provide us with simultaneous recordings of large neuronal populations, new methods for modelling such data must also be developed. We present a model of responses to repeated trials of a sensory stimulus based on thresholded Gaussian processes that allows for analysis and modelling of variability and covariability of population spike trains across multiple time scales. The model framework can be used to specify the values of many different variability measures including spike timing precision across trials, coefficient of variation of the interspike interval distribution, and Fano factor of spike counts for individual neurons, as well as signal and noise correlations and correlations of spike counts across multiple neurons.

Tell me something interesting: context dependent adaptation in somatosensory cortex.

It is widely accepted that through a process of adaptation cells adjust their sensitivity in accordance with prevailing stimulus conditions. However, in two recent studies exploring adaptation in the rodent inferior colliculus and somatosensory cortex, neurons did not adapt towards global mean, but rather became most sensitive to inputs that were located towards the edge of the stimulus distribution with greater intensity than the mean. We re-examined electrophysiological data from the somatosensory study with the purpose of exploring the underlying encoding strategies.

Emergence of tuning to natural stimulus statistics along the central auditory pathway.

We have previously shown that neurons in primary auditory cortex (A1) of anaesthetized (ketamine/medetomidine) ferrets respond more strongly and reliably to dynamic stimuli whose statistics follow "natural" 1/f dynamics than to stimuli exhibiting pitch and amplitude modulations that are faster (1/f(0.5)) or slower (1/f(2)) than 1/f. To investigate where along the central auditory pathway this 1/f-modulation tuning arises, we have now characterized responses of neurons in the central nucleus of the inferior colliculus (ICC) and the ventral division of the mediate geniculate nucleus of the thalamus (MGV) to 1/f(γ) distributed stimuli with γ varying between 0.

Current source density profiles of stimulus-specific adaptation in rat auditory cortex.

Neurons in primary auditory cortex (A1) are known to exhibit a phenomenon known as stimulus-specific adaptation (SSA), which means that, when tested with pure tones, they will respond more strongly to a particular frequency if it is presented as a rare, unexpected "oddball" stimulus than when the same stimulus forms part of a series of common, "standard" stimuli. Although SSA has occasionally been observed in midbrain neurons that form part of the paraleminscal auditory pathway, it is thought to be weak, rare, or nonexistent among neurons of the leminscal pathway that provide the main afferent input to A1, so that SSA seen in A1 is likely generated within A1 by local mechanisms. To study the contributions that neural processing within the different cytoarchitectonic layers of A1 may make to SSA, we recorded local field potentials in A1 of the rat in response to standard and oddball tones and subjected these to current source density analysis.

Response linearity in primary auditory cortex of the ferret.

The responses of neurons within the primary auditory cortex (A1) of the ferret elicited by broadband dynamic spectral ripple stimuli were examined over a range of ripple spectral densities and ripple velocities. The large majority of neurons showed modulated responses to these stimuli and responded most strongly at low ripple densities and velocities. The period histograms of their responses were subjected to Fourier analysis, and the ratio of the magnitudes of the f1 and fo (DC) components of these responses were calculated to give a quantitative index of response linearity.