Bidirectional Shifting Effects of the Sound Intensity on the Best Frequency in the Rat Auditory Cortex

Subcortical coding of predictable and unsupervised sound-context associations

Our environment is made of a myriad of stimuli present in combinations often patterned in predictable ways. For example, there is a strong association between where we are and the sounds we hear. Like many environmental patterns, sound-context associations are learned implicitly, in an unsupervised manner, and are highly informative and predictive of normality. Yet, we know little about where and how unsupervised sound-context associations are coded in the brain. Here we measured plasticity in the auditory midbrain of mice living over days in an enriched task-less environment in which entering a context triggered sound with different degrees of predictability. Plasticity in the auditory midbrain, a hub of auditory input and multimodal feedback, developed over days and reflected learning of contextual information in a manner that depended on the predictability of the sound-context association and not on reinforcement. Plasticity manifested as an increase in response gain and tuning shift that correlated with a general increase in neuronal frequency discrimination. Thus, the auditory midbrain is sensitive to unsupervised predictable sound-context associations, revealing a subcortical engagement in the detection of contextual sounds. By increasing frequency resolution, this detection might facilitate the processing of behaviorally relevant foreground information described to occur in cortical auditory structures.

A common computational principle for vibrotactile pitch perception in mouse and human

We live surrounded by vibrations generated by moving objects. These oscillatory stimuli propagate through solid substrates, are sensed by mechanoreceptors in our body and give rise to perceptual attributes such as vibrotactile pitch (i.e. the perception of how high or low a vibration’s frequency is). Here, we establish a mechanistic relationship between vibrotactile pitch perception and the physical properties of vibrations using behavioral tasks, in which vibratory stimuli were delivered to the human fingertip or the mouse forelimb. The resulting perceptual reports were analyzed with a model demonstrating that physically different combinations of vibration frequencies and amplitudes can produce equal pitch perception. We found that the perceptually indistinguishable but physically different stimuli follow a common computational principle in mouse and human. It dictates that vibrotactile pitch perception is shifted with increases in amplitude toward the frequency of highest vibrotactile sensitivity. These findings suggest the existence of a fundamental relationship between the seemingly unrelated concepts of spectral sensitivity and pitch perception.

The features of vibrations provide key information on the surrounding environment. Here the authors show that a common computational principle underlies vibrotactile pitch perception in both mice and humans.

Shaking Paws Is Not the Same as Shaking Hands

A recent Nature paper shows that activity in rodent forelimb somatosensory cortex is related to the animal's behavioral report of vibration intensity and identifies candidate mechanoreceptors responsible for the cortical response. Results highlight striking anatomical and neural differences from primates.

Feature-selective encoding of substrate vibrations in the forelimb somatosensory cortex

The spectral content of skin vibrations, produced by either displacing the finger across a surface texture¹ or passively sensing external movements through the solid substrate^2,3, provides fundamental information about our environment. Low-frequency flutter (below 50 Hz) applied locally to the primate fingertip evokes cyclically entrained spiking in neurons of the primary somatosensory cortex (S1), and thus spike rates in these neurons increase linearly with frequency^4,5. However, the same local vibrations at high frequencies (over 100 Hz) cannot be discriminated on the basis of differences in discharge rates of S1 neurons^4,6, because spiking is only partially entrained at these frequencies⁶. Here we investigated whether high-frequency substrate vibrations applied broadly to the mouse forelimb rely on a different cortical coding scheme. We found that forelimb S1 neurons encode vibration frequency similarly to sound pitch representation in the auditory cortex^7,8: their spike rates are selectively tuned to a preferred value of a low-level stimulus feature without any temporal entrainment. This feature, identified as the product of frequency and a power function of amplitude, was also found to be perceptually relevant as it predicted behaviour in a frequency discrimination task. Using histology, peripheral deafferentation and optogenetic receptor tagging, we show that these selective responses are inherited from deep Pacinian corpuscles located adjacent to bones, most densely around the ulna and radius and only sparsely along phalanges. This mechanoreceptor arrangement and the tuned cortical rate code suggest that the mouse forelimb constitutes a sensory channel best adapted for passive 'listening' to substrate vibrations, rather than for active texture exploration.

Fast Tonotopy Mapping of the Rat Auditory Cortex With a Custom-Made Electrode Array

We present a custom-made multielectrode array for the recording of evoked potentials during acute experiments in rats, which offers a quick and reliable estimation of the cortical tonotopy. The array consists of electrodes represented by insulated copper wires of 0.09 mm diameter fixed in epoxy resin in a 3 x 5 arrangement, with final impedances of 410-800 kOhm. The array was placed on the brain surface of anesthetized rats approximately at the location of the auditory cortex (AC) and the cortical evoked potentials (middle-latency responses, MLR) were elicited by a series of tone pips of different frequencies at 50 dB of sound pressure level (SPL) intensity. The frequency that evoked the highest MLR amplitude (best frequency, BF) was identified for each electrode. The obtained distribution of the BFs characterized the cortical tonotopy, and it correlated with the frequency selectivity of neurons recorded at the same positions by an extracellular microelectrode. Although the space resolution of the array did not allow for the identification of AC sub regions, the array proved to be a reliable tool for a quick estimation and prediction of areas of interest for the subsequent measurements of neurons by more precise techniques.

Auditory midbrain coding of statistical learning that results from discontinuous sensory stimulation

Detecting regular patterns in the environment, a process known as statistical learning, is essential for survival. Neuronal adaptation is a key mechanism in the detection of patterns that are continuously repeated across short (seconds to minutes) temporal windows. Here, we found in mice that a subcortical structure in the auditory midbrain was sensitive to patterns that were repeated discontinuously, in a temporally sparse manner, across windows of minutes to hours. Using a combination of behavioral, electrophysiological, and molecular approaches, we found changes in neuronal response gain that varied in mechanism with the degree of sound predictability and resulted in changes in frequency coding. Analysis of population activity (structural tuning) revealed an increase in frequency classification accuracy in the context of increased overlap in responses across frequencies. The increase in accuracy and overlap was paralleled at the behavioral level in an increase in generalization in the absence of diminished discrimination. Gain modulation was accompanied by changes in gene and protein expression, indicative of long-term plasticity. Physiological changes were largely independent of corticofugal feedback, and no changes were seen in upstream cochlear nucleus responses, suggesting a key role of the auditory midbrain in sensory gating. Subsequent behavior demonstrated learning of predictable and random patterns and their importance in auditory conditioning. Using longer timescales than previously explored, the combined data show that the auditory midbrain codes statistical learning of temporally sparse patterns, a process that is critical for the detection of relevant stimuli in the constant soundscape that the animal navigates through.

Some things are learned simply because they are there and not because they are relevant at that moment in time. This is particularly true of surrounding sounds, which we process automatically and continuously, detecting their repetitive patterns or singularities. Learning about rewards and punishment is typically attributed to cortical structures in the brain and known to occur over long time windows. Learning of surrounding regularities, on the other hand, is attributed to subcortical structures and has been shown to occur in seconds. The brain can, however, also detect the regularity in sounds that are discontinuously repeated across intervals of minutes and hours. For example, we learn to identify people by the sound of their steps through an unconscious process involving repeated but isolated exposures to the coappearance of sound and person. Here, we show that a subcortical structure, the auditory midbrain, can code such temporally spread regularities. Neurons in the auditory midbrain changed their response pattern in mice that heard a fixed tone whenever they went into one room in the environment they lived in. Learning of temporally spread sound patterns can, therefore, occur in subcortical structures.

Diversity in Excitation-Inhibition Mismatch Underlies Local Functional Heterogeneity in the Rat Auditory Cortex

Cortical neurons are heterogeneous in their functional properties. This heterogeneity is fundamental for the processing of different features of sensory information. However, functional diversity within a local group of neurons is poorly understood. Here, we demonstrate that neighboring cortical neurons in layer 5 but not those of layer 4 of the rat anterior auditory field (AAF) exhibited a surprisingly high level of diversity in tonal receptive fields. In vivo whole-cell voltage-clamp recordings revealed that the diversity of frequency representation was due to a spectral mismatch between synaptic excitation and inhibition to varying degrees. The spectral distribution of excitation was skewed at different levels, whereas inhibition was homogeneous and non-skewed, similar to the summed spiking activity of local neuronal ensembles, which further enhanced diversity. Our results indicate that AAF in the auditory cortex is involved in processing auditory information in a highly refined manner that is important for complex pattern recognition.