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Sounds can arise from the environment and also predictably from many of our own movements, such as vocalizing, walking, or playing music. The capacity to anticipate these movement-related (reafferent) sounds and distinguish them from environmental sounds is essential for normal hearing 1 , 2 , but the neural circuits that learn to anticipate the often arbitrary and changeable sounds that result from our movements remain largely unknown. Here we developed an acoustic virtual reality (aVR) system in which a mouse learned to associate a novel sound with its locomotor movements, allowing us to identify the neural circuit mechanisms that learn to suppress reafferent sounds and to probe the behavioural consequences of this predictable sensorimotor experience. We found that aVR experience gradually and selectively suppressed auditory cortical responses to the reafferent frequency, in part by strengthening motor cortical activation of auditory cortical inhibitory neurons that respond to the reafferent tone. This plasticity is behaviourally adaptive, as aVR-experienced mice showed an enhanced ability to detect non-reafferent tones during movement. Together, these findings describe a dynamic sensory filter that involves motor cortical inputs to the auditory cortex that can be shaped by experience to selectively suppress the predictable acoustic consequences of movement. Training of mice to associate a particular sound frequency with locomotion results in selective suppression of cortical responses to that frequency during movement, consistent with a motor-dependent form of auditory cortical plasticity.

The mismatch negativity (MMN) is a key biomarker of automatic deviance detection thought to emerge from 2 cortical sources. First, the auditory cortex (AC) encodes spectral regularities and reports frequency-specific deviances. Then, more abstract representations in the prefrontal cortex (PFC) allow to detect contextual changes of potential behavioral relevance. However, the precise location and time asynchronies between neuronal correlates underlying this frontotemporal network remain unclear and elusive. Our study presented auditory oddball paradigms along with “no-repetition” controls to record mismatch responses in neuronal spiking activity and local field potentials at the rat medial PFC. Whereas mismatch responses in the auditory system are mainly induced by stimulus-dependent effects, we found that auditory responsiveness in the PFC was driven by unpredictability, yielding context-dependent, comparatively delayed, more robust and longer-lasting mismatch responses mostly comprised of prediction error signaling activity. This characteristically different composition discarded that mismatch responses in the PFC could be simply inherited or amplified downstream from the auditory system. Conversely, it is more plausible for the PFC to exert top-down influences on the AC, since the PFC exhibited flexible and potent predictive processing, capable of suppressing redundant input more efficiently than the AC. Remarkably, the time course of the mismatch responses we observed in the spiking activity and local field potentials of the AC and the PFC combined coincided with the time course of the large-scale MMN-like signals reported in the rat brain, thereby linking the microscopic, mesoscopic, and macroscopic levels of automatic deviance detection.

Humans and other animals use spatial hearing to rapidly localize events in the environment. However, neural encoding of sound location is a complex process involving the computation and integration of multiple spatial cues that are not represented directly in the sensory organ (the cochlea). Our understanding of these mechanisms has increased enormously in the past few years. Current research is focused on the contribution of animal models for understanding human spatial audition, the effects of behavioural demands on neural sound location encoding, the emergence of a cue-independent location representation in the auditory cortex, and the relationship between single-source and concurrent location encoding in complex auditory scenes. Furthermore, computational modelling seeks to unravel how neural representations of sound source locations are derived from the complex binaural waveforms of real-life sounds. In this article, we review and integrate the latest insights from neurophysiological, neuroimaging and computational modelling studies of mammalian spatial hearing. We propose that the cortical representation of sound location emerges from recurrent processing taking place in a dynamic, adaptive network of early (primary) and higher-order (posterior–dorsal and dorsolateral prefrontal) auditory regions. This cortical network accommodates changing behavioural requirements and is especially relevant for processing the location of real-life, complex sounds and complex auditory scenes.

Cortical sensory maps are remodeled during early life to adapt to the surrounding environment. Both sensory and contextual signals are important for induction of this plasticity, but how these signals converge to sculpt developing thalamocortical circuits remains largely unknown. Here we show that layer 1 (L1) of primary auditory cortex (A1) is a key hub where neuromodulatory and topographically organized thalamic inputs meet to tune the cortical layers below. Inhibitory interneurons in L1 send narrowly descending projections to differentially modulate thalamic drive to pyramidal and parvalbumin-expressing (PV) cells in L4, creating brief windows of intracolumnar activation. Silencing of L1 (but not VIP-expressing) cells abolishes map plasticity during the tonotopic critical period. Developmental transitions in nicotinic acetylcholine receptor (nAChR) sensitivity in these cells caused by Lynx1 protein can be overridden to extend critical-period closure. Notably, thalamocortical maps in L1 are themselves stable, and serve as a scaffold for cortical plasticity throughout life.

Frequency-following responses (FFRs) are neurophonic potentials that provide a window into the encoding of complex sounds (e.g., speech/music), auditory disorders, and neuroplasticity. While the neural origins of the FFR remain debated, renewed controversy has reemerged after demonstration that FFRs recorded via magnetoencephalography (MEG) are dominated by cortical rather than brainstem structures as previously assumed. Here, we recorded high-density (64 ch) FFRs via EEG and applied state-of-the art source imaging techniques to multichannel data (discrete dipole modeling, distributed imaging, independent component analysis, computational simulations). Our data confirm a mixture of generators localized to bilateral auditory nerve (AN), brainstem inferior colliculus (BS), and bilateral primary auditory cortex (PAC). However, frequency-specific scrutiny of source waveforms showed the relative contribution of these nuclei to the aggregate FFR varied across stimulus frequencies. Whereas AN and BS sources produced robust FFRs up to ∼700 Hz, PAC showed weak phase-locking with little FFR energy above the speech fundamental (100 Hz). Notably, CLARA imaging further showed PAC activation was eradicated for FFRs >150 Hz, above which only subcortical sources remained active. Our results show (i) the site of FFR generation varies critically with stimulus frequency; and (ii) opposite the pattern observed in MEG, subcortical structures make the largest contribution to electrically recorded FFRs (AN ≥ BS > PAC). We infer that cortical dominance observed in previous neuromagnetic data is likely due to the bias of MEG to superficial brain tissue, underestimating subcortical structures that drive most of the speech-FFR. Cleanly separating subcortical from cortical FFRs can be achieved by ensuring stimulus frequencies are >150–200 Hz, above the phase-locking limit of cortical neurons.

During online speech processing, our brain tracks the acoustic fluctuations in speech at different timescales. Previous research has focused on generic timescales (for example, delta or theta bands) that are assumed to map onto linguistic features such as prosody or syllables. However, given the high intersubject variability in speaking patterns, such a generic association between the timescales of brain activity and speech properties can be ambiguous. Here, we analyse speech tracking in source-localised magnetoencephalographic data by directly focusing on timescales extracted from statistical regularities in our speech material. This revealed widespread significant tracking at the timescales of phrases (0.6-1.3 Hz), words (1.8-3 Hz), syllables (2.8-4.8 Hz), and phonemes (8-12.4 Hz). Importantly, when examining its perceptual relevance, we found stronger tracking for correctly comprehended trials in the left premotor (PM) cortex at the phrasal scale as well as in left middle temporal cortex at the word scale. Control analyses using generic bands confirmed that these effects were specific to the speech regularities in our stimuli. Furthermore, we found that the phase at the phrasal timescale coupled to power at beta frequency (13-30 Hz) in motor areas. This cross-frequency coupling presumably reflects top-down temporal prediction in ongoing speech perception. Together, our results reveal specific functional and perceptually relevant roles of distinct tracking and cross-frequency processes along the auditory-motor pathway.

Infant cries evoke powerful responses in parents 1 – 4 . Whether parental animals are intrinsically sensitive to neonatal vocalizations, or instead learn about vocal cues for parenting responses is unclear. In mice, pup-naive virgin females do not recognize the meaning of pup distress calls, but retrieve isolated pups to the nest after having been co-housed with a mother and litter 5 – 9 . Distress calls are variable, and require co-caring virgin mice to generalize across calls for reliable retrieval 10 , 11 . Here we show that the onset of maternal behaviour in mice results from interactions between intrinsic mechanisms and experience-dependent plasticity in the auditory cortex. In maternal females, calls with inter-syllable intervals (ISIs) from 75 to 375 milliseconds elicited pup retrieval, and cortical responses were generalized across these ISIs. By contrast, naive virgins were neuronally and behaviourally sensitized to the most common (‘prototypical’) ISIs. Inhibitory and excitatory neural responses were initially mismatched in the cortex of naive mice, with untuned inhibition and overly narrow excitation. During co-housing experiments, excitatory responses broadened to represent a wider range of ISIs, whereas inhibitory tuning sharpened to form a perceptual boundary. We presented synthetic calls during co-housing and observed that neurobehavioural responses adjusted to match these statistics, a process that required cortical activity and the hypothalamic oxytocin system. Neuroplastic mechanisms therefore build on an intrinsic sensitivity in the mouse auditory cortex, and enable rapid plasticity for reliable parenting behaviour. The onset of maternal behaviour in mice involves an interaction between intrinsic tuning of auditory cortical neurons and experience-dependent plasticity.

Neural activation patterns in the ventral visual cortex in response to different categories of visual stimuli (e.g., faces vs. houses) are less selective, or distinctive, in older adults than in younger adults, a phenomenon known as age-related neural dedifferentiation. In this study, we investigated whether neural dedifferentiation extends to the auditory cortex. Inspired by previous animal work, we also investigated whether individual differences in GABA are associated with individual differences in neural distinctiveness in humans. 20 healthy young adults (ages 18–29) and 23 healthy older adults (over 65) completed a functional magnetic resonance imaging (fMRI) scan, during which neural activity was estimated while they listened to music and foreign speech. GABA levels in the auditory, ventrovisual and sensorimotor cortex were estimated in the same individuals in a separate magnetic resonance spectroscopy (MRS) scan. Relative to the younger adults, the older adults exhibited both (1) less distinct activation patterns for music vs. speech stimuli and (2) lower GABA levels in the auditory cortex. Also, individual differences in auditory GABA levels (but not ventrovisual or sensorimotor GABA levels) were associated with individual differences in neural distinctiveness in the auditory cortex in the older adults. These results demonstrate that age-related neural dedifferentiation extends to the auditory cortex and suggest that declining GABA levels may play a role in neural dedifferentiation in older adults. •Older adults have less distinct activation patterns for music vs. foreign speech in auditory cortex than young adults.•Older adults also exhibit lower levels of the neurotransmitter GABA in the auditory cortex.•Individual differences in auditory GABA levels are associated with distinctiveness in the auditory cortex of older adults.

Calcium imaging data from mice performing a virtual reality auditory decision-making task are used to analyse the population codes in primary auditory and posterior parietal cortex that support choice behaviour. Timely coding in the cortex Information must be represented at many timescales in the cortex, from precise millisecond tracking of rapidly fluctuating inputs to seconds-long representation of behavioural choice variables. Using calcium imaging data from mice performing a virtual reality auditory decision-making task, Christopher Harvey and colleagues analyse the population codes in the primary auditory and posterior parietal cortex that support choice behaviour. Parietal cortex neurons have stronger activity correlations and carry information over longer timescales than neurons in the auditory cortex, revealing that correlation is a cortical property that enables information coding by populations over different timescales. The cortex represents information across widely varying timescales 1 , 2 , 3 , 4 , 5 . For instance, sensory cortex encodes stimuli that fluctuate over few tens of milliseconds 6 , 7 , whereas in association cortex behavioural choices can require the maintenance of information over seconds 8 , 9 . However, it remains poorly understood whether diverse timescales result mostly from features intrinsic to individual neurons or from neuronal population activity. This question remains unanswered, because the timescales of coding in populations of neurons have not been studied extensively, and population codes have not been compared systematically across cortical regions. Here we show that population codes can be essential to achieve long coding timescales. Furthermore, we find that the properties of population codes differ between sensory and association cortices. We compared coding for sensory stimuli and behavioural choices in auditory cortex and posterior parietal cortex as mice performed a sound localization task. Auditory stimulus information was stronger in auditory cortex than in posterior parietal cortex, and both regions contained choice information. Although auditory cortex and posterior parietal cortex coded information by tiling in time neurons that were transiently informative for approximately 200 milliseconds, the areas had major differences in functional coupling between neurons, measured as activity correlations that could not be explained by task events. Coupling among posterior parietal cortex neurons was strong and extended over long time lags, whereas coupling among auditory cortex neurons was weak and short-lived. Stronger coupling in posterior parietal cortex led to a population code with long timescales and a representation of choice that remained consistent for approximately 1 second. In contrast, auditory cortex had a code with rapid fluctuations in stimulus and choice information over hundreds of milliseconds. Our results reveal that population codes differ across cortex and that coupling is a variable property of cortical populations that affects the timescale of information coding and the accuracy of behaviour.

Learning causes a change in how information is processed by neuronal circuits. Whereas synaptic plasticity, an important cellular mechanism, has been studied in great detail, we know much less about how learning is implemented at the level of neuronal circuits and, in particular, how interactions between distinct types of neurons within local networks contribute to the process of learning. Here we show that acquisition of associative fear memories depends on the recruitment of a disinhibitory microcircuit in the mouse auditory cortex. Fear-conditioning-associated disinhibition in auditory cortex is driven by foot-shock-mediated cholinergic activation of layer 1 interneurons, in turn generating inhibition of layer 2/3 parvalbumin-positive interneurons. Importantly, pharmacological or optogenetic block of pyramidal neuron disinhibition abolishes fear learning. Together, these data demonstrate that stimulus convergence in the auditory cortex is necessary for associative fear learning to complex tones, define the circuit elements mediating this convergence and suggest that layer-1-mediated disinhibition is an important mechanism underlying learning and information processing in neocortical circuits. Stimulus convergence and concomitant auditory cortex disinhibition are essential for fear learning. Sounds like fear It is generally recognized that learned behavioural responses, such as those associated with sound, involve changes within specific neural circuits. However, we are only beginning to understand how those changes are implemented and what interactions between different types of neurons within the circuits contribute to the learning process. Using classical sound-based fear-conditioning in mice as a model system, Andreas Lüthi and colleagues identify a distinct disinhibition-based circuit that is critical to learning. The neural circuit involved is not specific to auditory cortex, and may represent a general mechanism through which cholinergic neuromodulation gates cortical activity.