White Noise Background Improves Tone Discrimination by Suppressing Cortical Tuning Curves

"Distinct inhibitory neurons differently shape neuronal codes for sound intensity in the auditory cortex"

Cortical circuits contain multiple types of inhibitory neurons which shape how information is processed within neuronal networks. Here, we asked whether somatostatin-expressing (SST) and vasoactive intestinal peptide-expressing (VIP) inhibitory neurons have distinct effects on population neuronal responses to noise bursts of varying intensities. We optogenetically stimulated SST or VIP neurons while simultaneously measuring the calcium responses of populations of hundreds of neurons in the auditory cortex of male and female awake, head-fixed mice to sounds. Upon SST neuronal activation, noise bursts representations became more discrete for different intensity levels, relying on cell identity rather than strength. By contrast, upon VIP neuronal activation, noise bursts of different intensity level activated overlapping neuronal populations, albeit at different response strengths. At the single-cell level, SST and VIP neuronal activation differentially activated the response-level curves of monotonic and nonmonotonic neurons. SST neuronal activation effects were consistent with a shift of the neuronal population responses toward a more localist code with different cells responding to sounds of different intensity. By contrast, VIP neuronal activation shifted responses towards a more distributed code, in which sounds of different intensity level are encoded in the relative response of similar populations of cells. These results delineate how distinct inhibitory neurons in the auditory cortex dynamically control cortical population codes. Different inhibitory neuronal populations may be recruited under different behavioral demands, depending on whether categorical or invariant representations are advantageous for the task.

Sequential maturation of stimulus-specific adaptation in the mouse lemniscal auditory system

Stimulus-specific adaptation (SSA), the reduction of neural activity to a common stimulus that does not generalize to other, rare stimuli, is an essential property of our brain. Although well characterized in adults, it is still unknown how it develops during adolescence and what neuronal circuits are involved. Using in vivo electrophysiology and optogenetics in the lemniscal pathway of the mouse auditory system, we observed SSA to be stable from postnatal day 20 (P20) in the inferior colliculus, to develop until P30 in the auditory thalamus and even later in the primary auditory cortex (A1). We found this maturation process to be experience-dependent in A1 but not in thalamus and to be related to alterations in deep but not input layers of A1. We also identified corticothalamic projections to be implicated in thalamic SSA development. Together, our results reveal different circuits underlying the sequential SSA maturation and provide a unique perspective to understand predictive coding and surprise across sensory systems.

Enhancing Performance of Convolutional Neural Network-Based Epileptic Electroencephalogram Diagnosis by Asymmetric Stochastic Resonance

Epilepsy is a chronic disorder that leads to transient neurological dysfunction and is clinically diagnosed primarily by electroencephalography. Several intelligent systems have been proposed to automatically detect seizures, among which deep convolutional neural networks (CNNs) have shown better performance than traditional machine-learning algorithms. Owing to artifacts and noise, the raw electroencephalogram (EEG) must be preprocessed to improve the signal-to-noise ratio prior to being fed into the CNN classifier. However, because of the spectrum overlapping of uncontrollable noise with EEG, traditional filters cause information loss in EEG; thus, the potential of classifiers cannot be fully exploited. In this study, we propose a stochastic resonance-effect-based EEG preprocessing module composed of three asymmetrical overdamped bistable systems in parallel. By setting different asymmetries for the three parallel units, the inherent noise can be transferred to the different spectral components of the EEG through the asymmetric stochastic resonance effect. In this process, the proposed preprocessing module not only avoids the loss of information of EEG but also provides a CNN with high-quality EEG of diversified frequency information to enhance its performance. By combining the proposed preprocessing module with a residual neural network, we developed an intelligent diagnostic system for predicting seizure onset. The developed system achieved an average sensitivity of 98.96% on the CHB-MIT dataset and 95.45% on the Siena dataset, with a false prediction rate of 0.048/h and 0.033/h, respectively. In addition, a comparative analysis demonstrated the superiority of the developed diagnostic system with the proposed preprocessing module over other existing methods.

Reduced connectivity of primary auditory and motor cortices during exposure to auditory white noise

Auditory white noise (WN) is widely used in daily life for inducing sleep, and in neuroscience to mask unwanted environmental noise and cues. However, WN was recently reported to influence corticospinal excitability and behavioral performance. Here, we expand previous preliminary findings on the influence of WN exposure on cortical functioning, and we hypothesize that it may modulate cortical connectivity. We tested our hypothesis by performing magnetoencephalography in 20 healthy subjects. WN reduces cortical connectivity of the primary auditory and motor regions with very distant cortical areas, showing a right lateralized connectivity reduction for primary motor cortex. The present results, together with previous finding concerning WN impact on corticospinal excitability and behavioral performance, further support the role of WN as a modulator of cortical function. This suggest avoiding its unrestricted use as a masking tool, while purposely designed and controlled WN application could be exploited to harness brain function and to treat neuropsychiatric conditions.

Effects of continuous tactile stimulation on auditory-evoked cortical responses depend on the audio-tactile phase

Auditory perception can benefit from stimuli in non-auditory sensory modalities, as for example in lip-reading. Compared with such visual influences, tactile influences are still poorly understood. It has been shown that single tactile pulses can enhance the perception of auditory stimuli depending on their relative timing, but whether and how such brief auditory enhancements can be stretched in time with more sustained, phase-specific periodic tactile stimulation is still unclear. To address this question, we presented tactile stimulation that fluctuated coherently and continuously at 4Hz with an auditory noise (either in-phase or anti-phase) and assessed its effect on the cortical processing and perception of an auditory signal embedded in that noise. Scalp-electroencephalography recordings revealed an enhancing effect of in-phase tactile stimulation on cortical responses phase-locked to the noise and a suppressive effect of anti-phase tactile stimulation on responses evoked by the auditory signal. Although these effects appeared to follow well-known principles of multisensory integration of discrete audio-tactile events, they were not accompanied by corresponding effects on behavioral measures of auditory signal perception. Our results indicate that continuous periodic tactile stimulation can enhance cortical processing of acoustically-induced fluctuations and mask cortical responses to an ongoing auditory signal. They further suggest that such sustained cortical effects can be insufficient for inducing sustained bottom-up auditory benefits.

Adaptation in auditory processing

Adaptation is an essential feature of auditory neurons, which reduces their responses to unchanging and recurring sounds and allows their response properties to be matched to the constantly changing statistics of sounds that reach the ears. As a consequence, processing in the auditory system highlights novel or unpredictable sounds and produces an efficient representation of the vast range of sounds that animals can perceive by continually adjusting the sensitivity and, to a lesser extent, the tuning properties of neurons to the most commonly encountered stimulus values. Together with attentional modulation, adaptation to sound statistics also helps to generate neural representations of sound that are tolerant to background noise and therefore plays a vital role in auditory scene analysis. In this review, we consider the diverse forms of adaptation that are found in the auditory system in terms of the processing levels at which they arise, the underlying neural mechanisms, and their impact on neural coding and perception. We also ask what the dynamics of adaptation, which can occur over multiple timescales, reveal about the statistical properties of the environment. Finally, we examine how adaptation to sound statistics is influenced by learning and experience and changes as a result of aging and hearing loss.

Background noise responding neurons in the inferior colliculus of the CF-FM bat, Hipposideros pratti

The Lombard effect, referring to an involuntary rise in vocal intensity, is a widespread vertebrate mechanism that aims to maintain signal efficiency in response to ambient noise. Previous studies showed that the Lombard effect could be sufficiently implemented at subcortical levels and operated by continuously monitoring background noise, requiring some subcortical auditory sensitive neurons to have continuous responses to background noise. However, such neurons have not been well characterized. The inferior colliculus (IC) is a major auditory integration center under the auditory cortex and provides projections to the putative vocal pattern generator in the brainstem. Thus, it is reasonable to speculate that the IC is a likely auditory nucleus candidate having background noise responding neurons (BNR neurons). In the present study, we isolated 183 sound-sensitive IC neurons in a constant frequency-frequency modulation bat, Hipposideros pratti, and found that around 19% of these IC neurons are BNR neurons when stimulated with 70 dB SPL background white noise. Their firing rates in response to noise increased with increasing noise intensity and could be suppressed by sound stimulation. Furthermore, compared to neurons with similar best frequencies, the BNR neurons had smaller Q_10-dB values and lower noise-induced minimal threshold change, indicating that BNR neurons received fewer inhibitory inputs. These results suggested that the BNR neurons are ideal candidates for collecting information about background noise. We proposed that the BNR neurons synapsed with neurons in vocal-pattern-generating networks in the brainstem and initiated the Lombard effect by a feed-forward loop.

Spatio-Temporally Efficient Coding Assigns Functions to Hierarchical Structures of the Visual System

Hierarchical structures constitute a wide array of brain areas, including the visual system. One of the important questions regarding visual hierarchical structures is to identify computational principles for assigning functions that represent the external world to hierarchical structures of the visual system. Given that visual hierarchical structures contain both bottom-up and top-down pathways, the derived principles should encompass these bidirectional pathways. However, existing principles such as predictive coding do not provide an effective principle for bidirectional pathways. Therefore, we propose a novel computational principle for visual hierarchical structures as spatio-temporally efficient coding underscored by the efficient use of given resources in both neural activity space and processing time. This coding principle optimises bidirectional information transmissions over hierarchical structures by simultaneously minimising temporal differences in neural responses and maximising entropy in neural representations. Simulations demonstrated that the proposed spatio-temporally efficient coding was able to assign the function of appropriate neural representations of natural visual scenes to visual hierarchical structures. Furthermore, spatio-temporally efficient coding was able to predict well-known phenomena, including deviations in neural responses to unlearned inputs and bias in preferred orientations. Our proposed spatio-temporally efficient coding may facilitate deeper mechanistic understanding of the computational processes of hierarchical brain structures.

Contributions of Distinct Auditory Cortical Inhibitory Neuron Types to the Detection of Sounds in Background Noise

The ability to separate background noise from relevant acoustic signals is essential for appropriate sound-driven behavior in natural environments. Examples of this separation are apparent in the auditory system, where neural responses to behaviorally relevant stimuli become increasingly noise invariant along the ascending auditory pathway. However, the mechanisms that underlie this reduction in responses to background noise are not well understood. To address this gap in knowledge, we first evaluated the effects of auditory cortical inactivation on mice of both sexes trained to perform a simple auditory signal-in-noise detection task and found that outputs from the auditory cortex are important for the detection of auditory stimuli in noisy environments. Next, we evaluated the contributions of the two most common cortical inhibitory cell types, parvalbumin-expressing (PV+) and somatostatin-expressing (SOM+) interneurons, to the perception of masked auditory stimuli. We found that inactivation of either PV+ or SOM+ cells resulted in a reduction in the ability of mice to determine the presence of auditory stimuli masked by noise. These results indicate that a disruption of auditory cortical network dynamics by either of these two types of inhibitory cells is sufficient to impair the ability to separate acoustic signals from noise.

Hearing in Complex Environments: Auditory Gain Control, Attention, and Hearing Loss

Listening in noisy or complex sound environments is difficult for individuals with normal hearing and can be a debilitating impairment for those with hearing loss. Extracting meaningful information from a complex acoustic environment requires the ability to accurately encode specific sound features under highly variable listening conditions and segregate distinct sound streams from multiple overlapping sources. The auditory system employs a variety of mechanisms to achieve this auditory scene analysis. First, neurons across levels of the auditory system exhibit compensatory adaptations to their gain and dynamic range in response to prevailing sound stimulus statistics in the environment. These adaptations allow for robust representations of sound features that are to a large degree invariant to the level of background noise. Second, listeners can selectively attend to a desired sound target in an environment with multiple sound sources. This selective auditory attention is another form of sensory gain control, enhancing the representation of an attended sound source while suppressing responses to unattended sounds. This review will examine both “bottom-up” gain alterations in response to changes in environmental sound statistics as well as “top-down” mechanisms that allow for selective extraction of specific sound features in a complex auditory scene. Finally, we will discuss how hearing loss interacts with these gain control mechanisms, and the adaptive and/or maladaptive perceptual consequences of this plasticity.

The Stop Signal Task for Measuring Behavioral Inhibition in Mice With Increased Sensitivity and High-Throughput Operation

Ceasing an ongoing motor response requires action cancelation. This is impaired in many pathologies such as attention deficit disorder and schizophrenia. Action cancelation is measured by the stop signal task that estimates how quickly a motor response can be stopped when it is already being executed. Apart from human studies, the stop signal task has been used to investigate neurobiological mechanisms of action cancelation overwhelmingly in rats and only rarely in mice, despite the need for a genetic model approach. Contributing factors to the limited number of mice studies may be the long and laborious training that is necessary and the requirement for a very loud (100 dB) stop signal. We overcame these limitations by employing a fully automated home-cage-based setup. We connected a home-cage to the operant box via a gating mechanism, that allowed individual ID chipped mice to start sessions voluntarily. Furthermore, we added a negative reinforcement consisting of a mild air puff with escape option to the protocol. This specifically improved baseline inhibition to 94% (from 84% with the conventional approach). To measure baseline inhibition the stop is signaled immediately with trial onset thus measuring action restraint rather than action cancelation ability. A high baseline allowed us to measure action cancelation ability with higher sensitivity. Furthermore, our setup allowed us to reduce the intensity of the acoustic stop signal from 100 to 70 dB. We constructed inhibition curves from stop trials with daily adjusted delays to estimate stop signal reaction times (SSRTs). SSRTs (median 88 ms) were lower than reported previously, which we attribute to the observed high baseline inhibition. Our automated training protocol reduced training time by 17% while also promoting minimal experimenter involvement. This sensitive and labor efficient stop signal task procedure should therefore facilitate the investigation of action cancelation pathologies in genetic mouse models.

Emergence and function of cortical offset responses in sound termination detection

Offset responses in auditory processing appear after a sound terminates. They arise in neuronal circuits within the peripheral auditory system, but their role in the central auditory system remains unknown. Here, we ask what the behavioral relevance of cortical offset responses is and what circuit mechanisms drive them. At the perceptual level, our results reveal that experimentally minimizing auditory cortical offset responses decreases the mouse performance to detect sound termination, assigning a behavioral role to offset responses. By combining in vivo electrophysiology in the auditory cortex and thalamus of awake mice, we also demonstrate that cortical offset responses are not only inherited from the periphery but also amplified and generated de novo. Finally, we show that offset responses code more than silence, including relevant changes in sound trajectories. Together, our results reveal the importance of cortical offset responses in encoding sound termination and detecting changes within temporally discontinuous sounds crucial for speech and vocalization.

Task-induced modulations of neuronal activity along the auditory pathway

Sensory processing varies depending on behavioral context. Here, we ask how task engagement modulates neurons in the auditory system. We train mice in a simple tone-detection task and compare their neuronal activity during passive hearing and active listening. Electrophysiological extracellular recordings in the inferior colliculus, medial geniculate body, primary auditory cortex, and anterior auditory field reveal widespread modulations across all regions and cortical layers and in both putative regular- and fast-spiking cortical neurons. Clustering analysis unveils ten distinct modulation patterns that can either enhance or suppress neuronal activity. Task engagement changes the tone-onset response in most neurons. Such modulations first emerge in subcortical areas, ruling out cortical feedback as the only mechanism underlying subcortical modulations. Half the neurons additionally display late modulations associated with licking, arousal, or reward. Our results reveal the presence of functionally distinct subclasses of neurons, differentially sensitive to specific task-related variables but anatomically distributed along the auditory pathway.

Inhibition in the auditory cortex

The auditory system provides us with extremely rich and precise information about the outside world. Once a sound reaches our ears, the acoustic information it carries travels from the cochlea all the way to the auditory cortex, where its complexity and nuances are integrated. In the auditory cortex, functional circuits are formed by subpopulations of intermingled excitatory and inhibitory cells. In this review, we discuss recent evidence of the specific contributions of inhibitory neurons in sound processing and integration. We first examine intrinsic properties of three main classes of inhibitory interneurons in the auditory cortex. Then, we describe how inhibition shapes the responsiveness of the auditory cortex to sound. Finally, we discuss how inhibitory interneurons contribute to the sensation and perception of sounds. Altogether, this review points out the crucial role of cortical inhibitory interneurons in integrating information about the context, history, or meaning of a sound. It also highlights open questions to be addressed for increasing our understanding of the staggering complexity leading to the subtlest auditory perception.

[Effects of background noise on auditory response characteristics of primary auditory cortex neurons in awake mice]

OBJECTIVE

To study the effects of different continuous background noises on auditory response characteristics of primary auditory cortex (A1) neurons in awake mice.

METHODS

We performed in vivo cell-attached recordings in layer 4 neurons of the A1 of awake mice to investigate how continuous background noises of different levels affected the intensity tuning, frequency tuning and time characteristics of individual A1 neurons. According to the intensity tuning characteristics and types of stimulation, 44 neurons were devided into 4 groups: monotonic-intensity group (20 monotonic neurons), nonmonotonic-intensity group (6 nonmonotonic neurons), monotonic-frequency group (25 monotonic neurons) and monotonic-latency group (15 monotonic neurons).

RESULTS

The A1 neurons only had transient spike response within 10 to 40 ms after the onset of continuous wild-band noise stimulation. The noise intensity had no significant effects on the background firing rates of the A1 neurons (P>0.05). The increase of background noise resulted in a significant linear elevation of the intensity threshold of monotonic and nonmonotonic neurons for tone-evoked response (R²>0.90, P < 0.05). No significant difference was observed in the slopes of threshold changes between monotonic and nonmonotonic neurons (P>0.05). The best intensity of nonmonotonic neurons increased along with the intensity of the background noise, and the variation of the best intensity was positively correlated with the change of the threshold of the same neuron (r=0.944, P < 0.001). The frequency response bandwidth and the firing rate of the A1 neurons decreased as the noise intensity increased (P < 0.001), but the best frequency almost remained unchanged (P < 0.001). The increase of background noise intensity resulted in an increased first spike latency of the neurons to the same tone stimulus (P < 0.05) without affecting the time accuracy of the first action potential (P>0.05).

CONCLUSION

The acoustic response threshold of the A1 neurons increases linearly with the increase of background noise intensity. An increased background noise leads to compressed frequency band-width, a decreased firing rate and a prolonged spike latency, but the frequency selectivity and the time accuracy of auditory response to the same noise remain stable.

Constant Resting Frequency and Auditory Midbrain Neuronal Frequency Analysis of Hipposideros pratti in Background White Noise

Acoustic communication signals are inevitably challenged by ambient noise. In response to noise, many animals adjust their calls to maintain signal detectability. However, the mechanisms by which the auditory system adapts to the adjusted pulses are unclear. Our previous study revealed that the echolocating bat, Hipposideros pratti, increased its pulse intensity in the presence of background white noise. In vivo single-neuron recording demonstrated that the auditory midbrain neurons tuned to the second harmonic (H2 neurons) increased their minimal threshold (MT) to a similar degree as the increment of pulse intensity in the presence of the background noise. Furthermore, the H2 neurons exhibited consistent spike rates at their best amplitudes and sharper intensity tuning with background white noise compared with silent conditions. The previous data indicated that sound intensity analysis by auditory midbrain neurons was adapted to the increased pulse intensity in the same noise condition. This study further examined the echolocation pulse frequency and frequency analysis of auditory midbrain neurons with noise conditions. The data revealed that H. pratti did not shift the resting frequency in the presence of background noise. The auditory midbrain neuronal frequency analysis highly linked to processing the resting frequency with the presence of noise by presenting the constant best frequency (BF), frequency sensitivity, and frequency selectivity. Thus, our results suggested that auditory midbrain neuronal responses in background white noise are adapted to process echolocation pulses in the noise conditions.

Cortical gamma-band resonance preferentially transmits coherent input

Synchronization has been implicated in neuronal communication, but causal evidence remains indirect. We use optogenetics to generate depolarizing currents in pyramidal neurons of the cat visual cortex, emulating excitatory synaptic inputs under precise temporal control, while measuring spike output. The cortex transforms constant excitation into strong gamma-band synchronization, revealing the well-known cortical resonance. Increasing excitation with ramps increases the strength and frequency of synchronization. Slow, symmetric excitation profiles reveal hysteresis of power and frequency. White-noise input sequences enable causal analysis of network transmission, establishing that the cortical gamma-band resonance preferentially transmits coherent input components. Models composed of recurrently coupled excitatory and inhibitory units uncover a crucial role of feedback inhibition and suggest that hysteresis can arise through spike-frequency adaptation. The presented approach provides a powerful means to investigate the resonance properties of local circuits and probe how these properties transform input and shape transmission.

Rhythmic synchronization has been implicated in neuronal communication, yet causal evidence has remained scarce. Lewis et al. optogenetically stimulate the visual cortex to emulate synaptic input while recording spike output. Cortex resonates at the gamma band (30–90 Hz) and preferentially transmits input that is coherent to the ongoing gamma-band rhythm.

Circadian fluctuations in glucocorticoid level predict perceptual discrimination sensitivity

Slow neurobiological rhythms, such as the circadian secretion of glucocorticoid (GC) hormones, modulate a variety of body functions. Whether and how endocrine fluctuations also exert an influence on perceptual abilities is largely uncharted. Here, we show that phasic increases in GC availability prove beneficial to auditory discrimination. In an age-varying sample of N = 68 healthy human participants, we characterize the covariation of saliva cortisol with perceptual sensitivity in an auditory pitch discrimination task at five time points across the sleep-wake cycle. First, momentary saliva cortisol levels were captured well by the time relative to wake-up and overall sleep duration. Second, within individuals, higher cortisol levels just prior to behavioral testing predicted better pitch discrimination ability, expressed as a steepened psychometric curve. This effect of GCs held under a set of statistical controls. Our results pave the way for more in-depth studies on neuroendocrinological determinants of sensory encoding and perception.

Cochlear neural degeneration disrupts hearing in background noise by increasing auditory cortex internal noise

Correlational evidence in humans suggests that selective difficulties hearing in noisy, social settings may reflect premature auditory nerve degeneration. Here, we induced primary cochlear neural degeneration (CND) in adult mice and found direct behavioral evidence for selective detection deficits in background noise. To identify central determinants for this perceptual disorder, we tracked daily changes in ensembles of layer 2/3 auditory cortex parvalbumin-expressing inhibitory neurons and excitatory pyramidal neurons with chronic two-photon calcium imaging. CND induced distinct forms of plasticity in cortical excitatory and inhibitory neurons that culminated in net hyperactivity, increased neural gain, and reduced adaptation to background noise. Ensemble activity measured while mice detected targets in noise could accurately decode whether individual behavioral trials were hits or misses. After CND, random surges of hypercorrelated cortical activity occurring just before target onset reliably predicted impending detection failures, revealing a source of internal cortical noise underlying perceptual difficulties in external noise.

Listening in complex acoustic scenes

Being able to pick out particular sounds, such as speech, against a background of other sounds represents one of the key tasks performed by the auditory system. Understanding how this happens is important because speech recognition in noise is particularly challenging for older listeners and for people with hearing impairments. Central to this ability is the capacity of neurons to adapt to the statistics of sounds reaching the ears, which helps to generate noise-tolerant representations of sounds in the brain. In more complex auditory scenes, such as a cocktail party — where the background noise comprises other voices, sound features associated with each source have to be grouped together and segregated from those belonging to other sources. This depends on precise temporal coding and modulation of cortical response properties when attending to a particular speaker in a multi-talker environment. Furthermore, the neural processing underlying auditory scene analysis is shaped by experience over multiple timescales.

General Enhancement of Spatial Hearing in Congenitally Blind People

Vision is thought to support the development of spatial abilities in the other senses. If this is true, how does spatial hearing develop in people lacking visual experience? We comprehensively addressed this question by investigating auditory-localization abilities in 17 congenitally blind and 17 sighted individuals using a psychophysical minimum-audible-angle task that lacked sensorimotor confounds. Participants were asked to compare the relative position of two sound sources located in central and peripheral, horizontal and vertical, or frontal and rear spaces. We observed unequivocal enhancement of spatial-hearing abilities in congenitally blind people, irrespective of the field of space that was assessed. Our results conclusively demonstrate that visual experience is not a prerequisite for developing optimal spatial-hearing abilities and that, in striking contrast, the lack of vision leads to a general enhancement of auditory-spatial skills.

Learning-related population dynamics in the auditory thalamus

Learning to associate sensory stimuli with a chosen action involves a dynamic interplay between cortical and thalamic circuits. While the cortex has been widely studied in this respect, how the thalamus encodes learning-related information is still largely unknown. We studied learning-related activity in the medial geniculate body (MGB; Auditory thalamus), targeting mainly the dorsal and medial regions. Using fiber photometry, we continuously imaged population calcium dynamics as mice learned a go/no-go auditory discrimination task. The MGB was tuned to frequency and responded to cognitive features like the choice of the mouse within several hundred milliseconds. Encoding of choice in the MGB increased with learning, and was highly correlated with the learning curves of the mice. MGB also encoded motor parameters of the mouse during the task. These results provide evidence that the MGB encodes task- motor- and learning-related information.

Computational and neurophysiological principles underlying auditory perceptual decisions

A fundamental scientific goal in auditory neuroscience is identifying what mechanisms allow the brain to transform an unlabeled mixture of auditory stimuli into distinct perceptual representations. This transformation is accomplished by a complex interaction of multiple neurocomputational processes, including Gestalt grouping mechanisms, categorization, attention, and perceptual decision-making. Despite a great deal of scientific energy devoted to understanding these principles of hearing, we still do not understand either how auditory perception arises from neural activity or the causal relationship between neural activity and auditory perception. Here, we review the contributions of cortical and subcortical regions to auditory perceptual decisions with an emphasis on those studies that simultaneously measure behavior and neural activity. We also put forth challenges to the field that must be faced if we are to further our understanding of the relationship between neural activity and auditory perception.