Sensitivity to complex statistical regularities in rat auditory cortex

Neural encoding of musical expectations in a non-human primate

The appreciation of music is a universal trait of humankind.1,2,3 Evidence supporting this notion includes the ubiquity of music across cultures4,5,6,7 and the natural predisposition toward music that humans display early in development.8,9,10 Are we musical animals because of species-specific predispositions? This question cannot be answered by relying on cross-cultural or developmental studies alone, as these cannot rule out enculturation.11 Instead, it calls for cross-species experiments testing whether homologous neural mechanisms underlying music perception are present in non-human primates. We present music to two rhesus monkeys, reared without musical exposure, while recording electroencephalography (EEG) and pupillometry. Monkeys exhibit higher engagement and neural encoding of expectations based on the previously seeded musical context when passively listening to real music as opposed to shuffled controls. We then compare human and monkey neural responses to the same stimuli and find a species-dependent contribution of two fundamental musical features-pitch and timing12-in generating expectations: while timing- and pitch-based expectations13 are similarly weighted in humans, monkeys rely on timing rather than pitch. Together, these results shed light on the phylogeny of music perception. They highlight monkeys' capacity for processing temporal structures beyond plain acoustic processing, and they identify a species-dependent contribution of time- and pitch-related features to the neural encoding of musical expectations.

A multimodal cortical network of sensory expectation violation revealed by fMRI

The brain is subjected to multi-modal sensory information in an environment governed by statistical dependencies. Mismatch responses (MMRs), classically recorded with EEG, have provided valuable insights into the brain's processing of regularities and the generation of corresponding sensory predictions. Only few studies allow for comparisons of MMRs across multiple modalities in a simultaneous sensory stream and their corresponding cross-modal context sensitivity remains unknown. Here, we used a tri-modal version of the roving stimulus paradigm in fMRI to elicit MMRs in the auditory, somatosensory and visual modality. Participants (N = 29) were simultaneously presented with sequences of low and high intensity stimuli in each of the three senses while actively observing the tri-modal input stream and occasionally reporting the intensity of the previous stimulus in a prompted modality. The sequences were based on a probabilistic model, defining transition probabilities such that, for each modality, stimuli were more likely to repeat (p = .825) than change (p = .175) and stimulus intensities were equiprobable (p = .5). Moreover, each transition was conditional on the configuration of the other two modalities comprising global (cross-modal) predictive properties of the sequences. We identified a shared mismatch network of modality general inferior frontal and temporo-parietal areas as well as sensory areas, where the connectivity (psychophysiological interaction) between these regions was modulated during mismatch processing. Further, we found deviant responses within the network to be modulated by local stimulus repetition, which suggests highly comparable processing of expectation violation across modalities. Moreover, hierarchically higher regions of the mismatch network in the temporo-parietal area around the intraparietal sulcus were identified to signal cross-modal expectation violation. With the consistency of MMRs across audition, somatosensation and vision, our study provides insights into a shared cortical network of uni- and multi-modal expectation violation in response to sequence regularities.

A Cortico-Striatal Circuit for Sound-Triggered Prediction of Reward Timing

A crucial aspect of auditory perception is the ability to use sound cues to predict future events and to time actions accordingly. For example, distinct smartphone notification sounds reflect a call that needs to be answered within a few seconds, or a text that can be read later; the sound of an approaching vehicle signals when it is safe to cross the street. Other animals similarly use sounds to plan, time and execute behaviors such as hunting, evading predation and tending to offspring. However, the neural mechanisms that underlie sound-guided prediction of upcoming salient event timing are not well understood. To address this gap, we employed an appetitive sound-triggered reward time prediction behavior in head-fixed mice. We find that mice trained on this task reliably estimate the time from a sound cue to upcoming reward on the scale of a few seconds, as demonstrated by learning-dependent well-timed increases in reward-predictive licking. Moreover, mice showed a dramatic impairment in their ability to use sound to predict delayed reward when the auditory cortex was inactivated, demonstrating its causal involvement. To identify the neurophysiological signatures of auditory cortical reward-timing prediction, we recorded local field potentials during learning and performance of this behavior and found that the magnitude of auditory cortical responses to the sound prospectively encoded the duration of the anticipated sound-reward time interval. Next, we explored how and where these sound-triggered time interval prediction signals propagate from the auditory cortex to time and initiate consequent action. We targeted the monosynaptic projections from the auditory cortex to the posterior striatum and found that chemogenetic inactivation of these projections impairs animal's ability to predict sound-triggered delayed reward. Simultaneous neural recordings in the auditory cortex and posterior striatum during task performance revealed coordination of neural activity across these regions during the sound cue predicting the time interval to reward. Collectively, our findings identify an auditory cortical-striatal circuit supporting sound-triggered timing-prediction behaviors.

Habituation, Adaptation and Prediction Processes in Neurodevelopmental Disorders: A Comprehensive Review

Habituation, the simplest form of learning preserved across species and evolution, is characterized by a response decrease as a stimulus is repeated. This adaptive function has been shown to be altered in some psychiatric and neurodevelopmental disorders such as autism spectrum disorder (ASD), attention-deficit/hyperactivity disorder (ADHD) or schizophrenia. At the brain level, habituation is characterized by a decrease in neural activity as a stimulation is repeated, referred to as neural adaptation. This phenomenon influences the ability to make predictions and to detect change, two processes altered in some neurodevelopmental and psychiatric disorders. In this comprehensive review, the objectives are to characterize habituation, neural adaptation, and prediction throughout typical development and in neurodevelopmental disorders; and to evaluate their implication in symptomatology, specifically in sensitivity to change or need for sameness. A summary of the different approaches to investigate adaptation will be proposed, in which we report the contribution of animal studies as well as electrophysiological studies in humans to understanding of underlying neuronal mechanisms.

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.

Hierarchical Deviant Processing in Auditory Cortex of Awake Mice

Detecting patterns, and noticing unexpected pattern changes, in the environment is a vital aspect of sensory processing. Adaptation and prediction error responses are two components of neural processing related to these tasks, and previous studies in the auditory system in rodents show that these two components are partially dissociable in terms of the topography and latency of neural responses to sensory deviants. However, many previous studies have focused on repetitions of single stimuli, such as pure tones, which have limited ecological validity. In this study, we tested whether the auditory cortical activity shows adaptation to repetition of more complex sound patterns (bisyllabic pairs). Specifically, we compared neural responses to violations of sequences based on single stimulus probability only, against responses to more complex violations based on stimulus order. We employed an auditory oddball paradigm and monitored the auditory cortex (ACtx) activity of awake mice (N=8) using wide-field calcium imaging. We found that cortical responses were sensitive both to single stimulus probabilities and to more global stimulus patterns, as mismatch signals were elicited following both substitution deviants and transposition deviants. Notably, A2 area elicited larger mismatch signaling to those deviants than primary ACtx (A1), which suggests a hierarchical gradient of prediction error signaling in the auditory cortex. Such a hierarchical gradient was observed for late but not early peaks of calcium transients to deviants, suggesting that the late part of the deviant response may reflect prediction error signaling in response to more complex sensory pattern violations.

Separate Functional Subnetworks of Excitatory Neurons Show Preference to Periodic and Random Sound Structures

Auditory cortex (ACX) neurons are sensitive to spectro-temporal sound patterns and violations in patterns induced by rare stimuli embedded within streams of sounds. We investigate the auditory cortical representation of repeated presentations of sequences of sounds with standard stimuli (common) with an embedded deviant (rare) stimulus in two conditions, Periodic (Fixed deviant position) or Random (Random deviant position). We used extracellular single-unit and two-photon Ca2+ imaging recordings in layer 2/3 neurons of the mouse (Mus musculus) ACX of either sex. Population single-unit average responses increased over repetitions in the Random condition and were suppressed or did not change in the Periodic condition, showing general irregularity preference. A subset of neurons showed the opposite behavior, indicating regularity preference. Furthermore, pairwise noise correlations were higher in the Random condition than in the Periodic condition, suggesting a role of recurrent connections in the observed differential adaptation. Functional two-photon Ca2+ imaging showed that excitatory (EX), and inhibitory (IN) neurons [parvalbumin-positive (PV) and somatostatin-positive (SOM)] also had different categories of long-term adaptation as observed with single-units. However, examination of functional connectivity between pairs of neurons of different categories showed that EX-PV connected pairs behaved opposite to the EX-EX and EX-SOM pairs, with more connections outside category in Random condition than Periodic condition. Finally, considering Regularity, Irregularity, and no preference of connected pairs of neurons showed that EX-EX and EX-SOM pairs were in largely separate functional subnetworks with different preferences, not EX-PV pairs. Thus, separate subnetworks underlie coding of periodic and random sound sequences.SIGNIFICANCE STATEMENT Studying how the auditory cortex (ACX) neurons respond to streams of sound sequences help us understand the importance of changes in dynamic acoustic noisy scenes around us. Humans and animals are sensitive to regularity and its violations in sound sequences. Psychophysical tasks in humans show that the auditory brain differentially responds to Periodic and Random structures, independent of the listener's attentional states. Here, we show that mouse ACX L2/3 neurons detect changes and respond differently to patterns over long-time scales. The differential functional connectivity profile obtained in response to two different sound contexts suggests the vital role of recurrent connections in the auditory cortical network. Furthermore, the excitatory-inhibitory neuronal interactions can contribute to detecting the changing sound patterns.

Cortico-fugal regulation of predictive coding

Sensory systems must account for both contextual factors and prior experience to adaptively engage with the dynamic external environment. In the central auditory system, neurons modulate their responses to sounds based on statistical context. These response modulations can be understood through a hierarchical predictive coding lens: responses to repeated stimuli are progressively decreased, in a process known as repetition suppression, whereas unexpected stimuli produce a prediction error signal. Prediction error incrementally increases along the auditory hierarchy from the inferior colliculus (IC) to the auditory cortex (AC), suggesting that these regions may engage in hierarchical predictive coding. A potential substrate for top-down predictive cues is the massive set of descending projections from the auditory cortex to subcortical structures, although the role of this system in predictive processing has never been directly assessed. We tested the effect of optogenetic inactivation of the auditory cortico-collicular feedback in awake mice on responses of IC neurons to stimuli designed to test prediction error and repetition suppression. Inactivation of the cortico-collicular pathway led to a decrease in prediction error in IC. Repetition suppression was unaffected by cortico-collicular inactivation, suggesting that this metric may reflect fatigue of bottom-up sensory inputs rather than predictive processing. We also discovered populations of IC units that exhibit repetition enhancement, a sequential increase in firing with stimulus repetition. Cortico-collicular inactivation led to a decrease in repetition enhancement in the central nucleus of IC, suggesting that it is a top-down phenomenon. Negative prediction error, a stronger response to a tone in a predictable rather than unpredictable sequence, was suppressed in shell IC units during cortico-collicular inactivation. These changes in predictive coding metrics arose from bidirectional modulations in the response to the standard and deviant contexts, such that units in IC responded more similarly to each context in the absence of cortical input. We also investigated how these metrics compare between the anesthetized and awake states by recording from the same units under both conditions. We found that metrics of predictive coding and deviance detection differ depending on the anesthetic state of the animal, with negative prediction error emerging in the central IC and repetition enhancement and prediction error being more prevalent in the absence of anesthesia. Overall, our results demonstrate that the auditory cortex provides cues about the statistical context of sound to subcortical brain regions via direct feedback, regulating processing of both prediction and repetition.

Novel stimuli evoke excess activity in the mouse primary visual cortex

Rapid detection and processing of stimulus novelty are key elements of adaptive behavior. Predictive coding theories postulate that novel stimuli should be encoded differently from familiar stimuli. Here, we show that the majority of neurons in layer 2/3 of the mouse primary visual cortex exhibit a significant excess response to novel visual stimuli. The distinction between novel and familiar images developed rapidly, requiring only a few repeated presentations. We show that this phenomenon can be described by a model of cascading adaptation. This ubiquitous mechanism makes it likely that similar computations could be carried out in many brain areas.

To explore how neural circuits represent novel versus familiar inputs, we presented mice with repeated sets of images with novel images sparsely substituted. Using two-photon calcium imaging to record from layer 2/3 neurons in the mouse primary visual cortex, we found that novel images evoked excess activity in the majority of neurons. This novelty response rapidly emerged, arising with a time constant of 2.6 ± 0.9 s. When a new image set was repeatedly presented, a majority of neurons had similarly elevated activity for the first few presentations, which decayed to steady state with a time constant of 1.4 ± 0.4 s. When we increased the number of images in the set, the novelty response’s amplitude decreased, defining a capacity to store ∼15 familiar images under our conditions. These results could be explained quantitatively using an adaptive subunit model in which presynaptic neurons have individual tuning and gain control. This result shows that local neural circuits can create different representations for novel versus familiar inputs using generic, widely available mechanisms.

The generation of cortical novelty responses through inhibitory plasticity

Animals depend on fast and reliable detection of novel stimuli in their environment. Neurons in multiple sensory areas respond more strongly to novel in comparison to familiar stimuli. Yet, it remains unclear which circuit, cellular, and synaptic mechanisms underlie those responses. Here, we show that spike-timing-dependent plasticity of inhibitory-to-excitatory synapses generates novelty responses in a recurrent spiking network model. Inhibitory plasticity increases the inhibition onto excitatory neurons tuned to familiar stimuli, while inhibition for novel stimuli remains low, leading to a network novelty response. The generation of novelty responses does not depend on the periodicity but rather on the distribution of presented stimuli. By including tuning of inhibitory neurons, the network further captures stimulus-specific adaptation. Finally, we suggest that disinhibition can control the amplification of novelty responses. Therefore, inhibitory plasticity provides a flexible, biologically plausible mechanism to detect the novelty of bottom-up stimuli, enabling us to make experimentally testable predictions.

Attention modulates repetition effects in a context of low periodicity

Stimulus repetition can result in a reduction in neural responses (i.e., repetition suppression) in neuroimaging studies. Predictive coding models of perception postulate that this phenomenon largely reflects the top-down attenuation of prediction errors. Electroencephalography research further demonstrated that repetition effects consist of sequentially ordered attention-independent and attention-dependent components in a context of high periodicity. However, the statistical structure of our auditory environment is richer than that of a fixed pattern. It remains unclear if the attentional modulation of repetition effects can be generalised to a setting which better represents the nature of our auditory environment. Here we used electroencephalography to investigate whether the attention-independent and attention-dependent components of repetition effects previously described in the auditory modality remain in a context of low periodicity where temporary disruption might be absent/present. Participants were presented with repetition trains of various lengths, with/without temporary disruptions. We found attention-independent and attention-dependent repetition effects on, respectively, the P2 and P3a event-related potential components. This pattern of results is in line with previous research, confirming that the attenuation of prediction errors upon stimulus repetition is first registered regardless of attentional state before further attenuation of attended but not unattended prediction errors takes place. However, unlike previous reports, these effects manifested on later components. This divergence from previous studies is discussed in terms of the possible contribution of contextual factors.

Magnetoencephalography Responses to Unpredictable and Predictable Rare Somatosensory Stimuli in Healthy Adult Humans

Mismatch brain responses to unpredicted rare stimuli are suggested to be a neural indicator of prediction error, but this has rarely been studied in the somatosensory modality. Here, we investigated how the brain responds to unpredictable and predictable rare events. Magnetoencephalography responses were measured in adults frequently presented with somatosensory stimuli (FRE) that were occasionally replaced by two consecutively presented rare stimuli [unpredictable rare stimulus (UR) and predictable rare stimulus (PR); p = 0.1 for each]. The FRE and PR were electrical stimulations administered to either the little finger or the forefinger in a counterbalanced manner between the two conditions. The UR was a simultaneous electrical stimulation to both the forefinger and the little finger (for a smaller subgroup, the UR and FRE were counterbalanced for the stimulus properties). The grand-averaged responses were characterized by two main components: one at 30-100 ms (M55) and the other at 130-230 ms (M150) latency. Source-level analysis was conducted for the primary somatosensory cortex (SI) and the secondary somatosensory cortex (SII). The M55 responses were larger for the UR and PR than for the FRE in both the SI and the SII areas and were larger for the UR than for the PR. For M150, both investigated areas showed increased activity for the UR and the PR compared to the FRE. Interestingly, although the UR was larger in stimulus energy (stimulation of two fingers at the same time) and had a larger prediction error potential than the PR, the M150 responses to these two rare stimuli did not differ in source strength in either the SI or the SII area. The results suggest that M55, but not M150, can possibly be associated with prediction error signals. These findings highlight the need for disentangling prediction error and rareness-related effects in future studies investigating prediction error signals.

Limited Evidence for Sensory Prediction Error Responses in Visual Cortex of Macaques and Humans

A recent formulation of predictive coding theory proposes that a subset of neurons in each cortical area encodes sensory prediction errors, the difference between predictions relayed from higher cortex and the sensory input. Here, we test for evidence of prediction error responses in spiking responses and local field potentials (LFP) recorded in primary visual cortex and area V4 of macaque monkeys, and in complementary electroencephalographic (EEG) scalp recordings in human participants. We presented a fixed sequence of visual stimuli on most trials, and violated the expected ordering on a small subset of trials. Under predictive coding theory, pattern-violating stimuli should trigger robust prediction errors, but we found that spiking, LFP and EEG responses to expected and pattern-violating stimuli were nearly identical. Our results challenge the assertion that a fundamental computational motif in sensory cortex is to signal prediction errors, at least those based on predictions derived from temporal patterns of visual stimulation.

Inverted central auditory hierarchies for encoding local intervals and global temporal patterns

In sensory systems, representational features of increasing complexity emerge at successive stages of processing. In the mammalian auditory pathway, the clearest change from brainstem to cortex is defined by what is lost, not by what is gained, in that high-fidelity temporal coding becomes increasingly restricted to slower acoustic modulation rates.^1,2 Here, we explore the idea that sluggish temporal processing is more than just an inability for fast processing, but instead reflects an emergent specialization for encoding sound features that unfold on very slow timescales.^3,4 We performed simultaneous single unit ensemble recordings from three hierarchical stages of auditory processing in awake mice - the inferior colliculus (IC), medial geniculate body of the thalamus (MGB) and primary auditory cortex (A1). As expected, temporal coding of brief local intervals (0.001 - 0.1 s) separating consecutive noise bursts was robust in the IC and declined across MGB and A1. By contrast, slowly developing (∼1 s period) global rhythmic patterns of inter-burst interval sequences strongly modulated A1 spiking, were weakly captured by MGB neurons, and not at all by IC neurons. Shifts in stimulus regularity were not represented by changes in A1 spike rates, but rather in how the spikes were arranged in time. These findings show that low-level auditory neurons with fast timescales encode isolated sound features but not the longer gestalt, while the extended timescales in higher-level areas can facilitate sensitivity to slower contextual changes in the sensory environment.

Effects of noise precursors on the detection of amplitude and frequency modulation for tones in noise

Recent studies on amplitude modulation (AM) detection for tones in noise reported that AM-detection thresholds improve when the AM stimulus is preceded by a noise precursor. The physiological mechanisms underlying this AM unmasking are unknown. One possibility is that adaptation to the level of the noise precursor facilitates AM encoding by causing a shift in neural rate-level functions to optimize level encoding around the precursor level. The aims of this study were to investigate whether such a dynamic-range adaptation is a plausible mechanism for the AM unmasking and whether frequency modulation (FM), thought to be encoded via AM, also exhibits the unmasking effect. Detection thresholds for AM and FM of tones in noise were measured with and without a fixed-level precursor. Listeners showing the unmasking effect were then tested with the precursor level roved over a wide range to modulate the effect of adaptation to the precursor level on the detection of the subsequent AM. It was found that FM detection benefits from a precursor and the magnitude of FM unmasking correlates with that of AM unmasking. Moreover, consistent with dynamic-range adaptation, the unmasking magnitude weakens as the level difference between the precursor and simultaneous masker of the tone increases.

Sensitivity to temporal structure facilitates perceptual analysis of complex auditory scenes

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Perception relies on sensitivity to predictable structure in the environment.

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We used artificial acoustic scenes to investigate this in the auditory modality.

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Listeners track the temporal structure of multiple concurrent acoustic streams.

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Sensitivity to predictable structure supports auditory scene analysis, even when scenes are complex.

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Benefit of regularity observed even when listeners are unaware of the predictable structure.

The notion that sensitivity to the statistical structure of the environment is pivotal to perception has recently garnered considerable attention. Here we investigated this issue in the context of hearing. Building on previous work (Sohoglu and Chait, 2016a; elife), stimuli were artificial ‘soundscapes’ populated by multiple (up to 14) simultaneous streams (‘auditory objects’) comprised of tone-pip sequences, each with a distinct frequency and pattern of amplitude modulation. Sequences were either temporally regular or random.

We show that listeners’ ability to detect abrupt appearance or disappearance of a stream is facilitated when scene streams were characterized by a temporally regular fluctuation pattern. The regularity of the changing stream as well as that of the background (non-changing) streams contribute independently to this effect. Remarkably, listeners benefit from regularity even when they are not consciously aware of it. These findings establish that perception of complex acoustic scenes relies on the availability of detailed representations of the regularities automatically extracted from multiple concurrent streams.

Early cortical processing of pitch height and the role of adaptation and musicality

Pitch is an important perceptual feature; however, it is poorly understood how its cortical correlates are shaped by absolute vs relative fundamental frequency (f₀), and by neural adaptation. In this study, we assessed transient and sustained auditory evoked fields (AEFs) at the onset, progression, and offset of short pitch height sequences, taking into account the listener's musicality. We show that neuromagnetic activity reflects absolute f₀ at pitch onset and offset, and relative f₀ at transitions within pitch sequences; further, sequences with fixed f₀ lead to larger response suppression than sequences with variable f₀ contour, and to enhanced offset activity. Musical listeners exhibit stronger f₀-related AEFs and larger differences between their responses to fixed vs variable sequences, both within sequences and at pitch offset. The results resemble prominent psychoacoustic phenomena in the perception of pitch contours; moreover, they suggest a strong influence of adaptive mechanisms on cortical pitch processing which, in turn, might be modulated by a listener's musical expertise.

The effect of healthy aging on change detection and sensitivity to predictable structure in crowded acoustic scenes

The auditory system plays a critical role in supporting our ability to detect abrupt changes in our surroundings. Here we study how this capacity is affected in the course of healthy ageing. Artifical acoustic 'scenes', populated by multiple concurrent streams of pure tones ('sources') were used to capture the challenges of listening in complex acoustic environments. Two scene conditions were included: REG scenes consisted of sources characterized by a regular temporal structure. Matched RAND scenes contained sources which were temporally random. Changes, manifested as the abrupt disappearance of one of the sources, were introduced to a subset of the trials and participants ('young' group N = 41, age 20-38 years; 'older' group N = 41, age 60-82 years) were instructed to monitor the scenes for these events. Previous work demonstrated that young listeners exhibit better change detection performance in REG scenes, reflecting sensitivity to temporal structure. Here we sought to determine: (1) Whether 'baseline' change detection ability (i.e. in RAND scenes) is affected by age. (2) Whether aging affects listeners' sensitivity to temporal regularity. (3) How change detection capacity relates to listeners' hearing and cognitive profile (a battery of tests that capture hearing and cognitive abilities hypothesized to be affected by aging). The results demonstrated that healthy aging is associated with reduced sensitivity to abrupt scene changes in RAND scenes but that performance does not correlate with age or standard audiological measures such as pure tone audiometry or speech in noise performance. Remarkably older listeners' change detection performance improved substantially (up to the level exhibited by young listeners) in REG relative to RAND scenes. This suggests that the ability to extract and track the regularity associated with scene sources, even in crowded acoustic environments, is relatively preserved in older listeners.

Adaptation Modulates Spike-Phase Coupling Tuning Curve in the Rat Primary Auditory Cortex

Adaptation is an important mechanism that causes a decrease in the neural response both in terms of local field potentials (LFP) and spiking activity. We previously showed this reduction effect in the tuning curve of the primary auditory cortex. Moreover, we revealed that a repeated stimulus reduces the neural response in terms of spike-phase coupling (SPC). In the current study, we examined the effect of adaptation on the SPC tuning curve. To this end, employing the phase-locking value (PLV) method, we estimated the spike-LFP coupling. The data was obtained by a simultaneous recording from four single-electrodes in the primary auditory cortex of 15 rats. We first investigated whether the neural system may use spike-LFP phase coupling in the primary auditory cortex to encode sensory information. Secondly, we investigated the effect of adaptation on this potential SPC tuning. Our data showed that the coupling between spikes’ times and the LFP phase in beta oscillations represents sensory information (different stimulus frequencies), with an inverted bell-shaped tuning curve. Furthermore, we showed that adaptation to a specific frequency modulates SPC tuning curve of the adapter and its neighboring frequencies. These findings could be useful for interpretation of feature representation in terms of SPC and the underlying neural mechanism of adaptation.

Statistical learning of transition patterns in the songbird auditory forebrain

Statistical learning of transition patterns between sounds—a striking capability of the auditory system—plays an essential role in animals’ survival (e.g., detect deviant sounds that signal danger). However, the neural mechanisms underlying this capability are still not fully understood. We recorded extracellular multi-unit and single-unit activity in the auditory forebrain of awake male zebra finches while presenting rare repetitions of a single sound in a long sequence of sounds (canary and zebra finch song syllables) patterned in either an alternating or random order at different inter-stimulus intervals (ISI). When preceding stimuli were regularly alternating (alternating condition), a repeated stimulus violated the preceding transition pattern and was a deviant. When preceding stimuli were in random order (control condition), a repeated stimulus did not violate any regularities and was not a deviant. At all ISIs tested (1 s, 3 s, or jittered at 0.8–1.2 s), deviant repetition enhanced neural responses in the alternating condition in a secondary auditory area (caudomedial nidopallium, NCM) but not in the primary auditory area (Field L2); in contrast, repetition suppressed responses in the control condition in both Field L2 and NCM. When stimuli were presented in the classical oddball paradigm at jittered ISI (0.8–1.2 s), neural responses in both NCM and Field L2 were stronger when a stimulus occurred as deviant with low probability than when the same stimulus occurred as standard with high probability. Together, these results demonstrate: (1) classical oddball effect exists even when ISI is jittered and the onset of a stimulus is not fully predictable; (2) neurons in NCM can learn transition patterns between sounds at multiple ISIs and detect violation of these transition patterns; (3) sensitivity to deviant sounds increases from Field L2 to NCM in the songbird auditory forebrain. Further studies using the current paradigms may help us understand the neural substrate of statistical learning and even speech comprehension.

Stimulus-specific adaptation to behaviorally-relevant sounds in awake rats

Stimulus-specific adaptation (SSA) is the reduction in responses to a common stimulus that does not generalize, or only partially generalizes, to other stimuli. SSA has been studied mainly with sounds that bear no behavioral meaning. We hypothesized that the acquisition of behavioral meaning by a sound should modify the amount of SSA evoked by that sound. To test this hypothesis, we used fear conditioning in rats, using two word-like stimuli, derived from the English words "danger" and "safety", as well as pure tones. One stimulus (CS+) was associated with a foot shock whereas the other stimulus (CS-) was presented without a concomitant foot shock. We recorded neural responses to the auditory stimuli telemetrically, using chronically implanted multi-electrode arrays in freely moving animals before and after conditioning. Consistent with our hypothesis, SSA changed in a way that depended on the behavioral role of the sound: the contrast between standard and deviant responses remained the same or decreased for CS+ stimuli but increased for CS- stimuli, showing that SSA is shaped by experience. In most cases the sensory responses underlying these changes in SSA increased following conditioning. Unexpectedly, the responses to CS+ word-like stimuli showed a specific, large decrease, which we interpret as evidence for substantial inhibitory plasticity.

Synaptic Recruitment Enhances Gap Termination Responses in Auditory Cortex

The ability to detect short gaps in noise is an important tool for assessing the temporal resolution in the auditory cortex. However, the mere existence of responses to temporal gaps bounded by two short broadband markers is surprising, because of the expected short-term suppression that is prevalent in auditory cortex. Here, we used in-vivo intracellular recordings in anesthetized rats to dissect the synaptic mechanisms that underlie gap-related responses. When a gap is bounded by two short markers, a gap termination response was evoked by the onset of the second marker with minimal contribution from the offset of the first marker. Importantly, we show that the gap termination response was driven by a different (potentially partially overlapping) synaptic population than that underlying the onset response to the first marker. This recruitment of additional synaptic resources is a novel mechanism contributing to the important perceptual task of gap detection.

Autocorrelation Structure in the Macaque Dorsolateral, But not Orbital or Polar, Prefrontal Cortex Predicts Response-Coding Strength in a Visually Cued Strategy Task

In previous work, we studied the activity of neurons in the dorsolateral (PFdl), orbital (PFo), and polar (PFp) prefrontal cortex while monkeys performed a strategy task with 2 spatial goals. A cue instructed 1 of 2 strategies in each trial: stay with the previous goal or shift to the alternative goal. Each trial started with a fixation period, followed by a cue. Subsequently, a delay period was followed by a "go" signal that instructed the monkeys to choose one goal. After each choice, feedback was provided. In this study, we focused on the temporal receptive fields of the neurons, as measured by the decay in autocorrelation (time constant) during the fixation period, and examined the relationship with response and strategy coding. The temporal receptive field in PFdl correlated with the response-related but not with the strategy-related modulation in the delay and the feedback periods: neurons with longer time constants in PFdl tended to show stronger and more prolonged response coding. No such correlation was found in PFp or PFo. These findings demonstrate that the temporal specialization of neurons for temporally extended computations is predictive of response coding, and neurons in PFdl, but not PFp or PFo, develop such predictive properties.

Using Neural Circuit Interrogation in Rodents to Unravel Human Speech Decoding

The neural circuits responsible for social communication are among the least understood in the brain. Human studies have made great progress in advancing our understanding of the global computations required for processing speech, and animal models offer the opportunity to discover evolutionarily conserved mechanisms for decoding these signals. In this review article, we describe some of the most well-established speech decoding computations from human studies and describe animal research designed to reveal potential circuit mechanisms underlying these processes. Human and animal brains must perform the challenging tasks of rapidly recognizing, categorizing, and assigning communicative importance to sounds in a noisy environment. The instructions to these functions are found in the precise connections neurons make with one another. Therefore, identifying circuit-motifs in the auditory cortices and linking them to communicative functions is pivotal. We review recent advances in human recordings that have revealed the most basic unit of speech decoded by neurons is a phoneme, and consider circuit-mapping studies in rodents that have shown potential connectivity schemes to achieve this. Finally, we discuss other potentially important processing features in humans like lateralization, sensitivity to fine temporal features, and hierarchical processing. The goal is for animal studies to investigate neurophysiological and anatomical pathways responsible for establishing behavioral phenotypes that are shared between humans and animals. This can be accomplished by establishing cell types, connectivity patterns, genetic pathways and critical periods that are relevant in the development and function of social communication.

Mice tune out not in: violation of prediction drives auditory saliency

Successful navigation in complex acoustic scenes requires focusing on relevant sounds while ignoring irrelevant distractors. It has been argued that the ability to track stimulus statistics and generate predictions supports the choice of what to attend and what to ignore. However, the role of these predictions about future auditory events in drafting decisions remains elusive. While most psychophysical studies in humans indicate that expected stimuli are more easily detected, most work studying physiological auditory processing in animals highlights the detection of unexpected, surprising stimuli. Here, we tested whether in the mouse, high target probability results in enhanced detectability or whether detection is biased towards low-probability deviants using an auditory detection task. We implemented a probabilistic choice model to investigate whether a possible dependence on stimulus statistics arises from short-term serial correlations or from integration over longer periods. Our results demonstrate that target detectability in mice decreases with increasing probability, contrary to humans. We suggest that mice indeed track probability over a timescale of at least several minutes but do not use this information in the same way as humans do: instead of maximizing reward by focusing on high-probability targets, the saliency of a target is determined by surprise.

A generic deviance detection principle for cortical On/Off responses, omission response, and mismatch negativity

Neural responses to sudden changes can be observed in many parts of the sensory pathways at different organizational levels. For example, deviants that violate regularity at various levels of abstraction can be observed as simple On/Off responses of individual neurons or as cumulative responses of neural populations. The cortical deviance-related responses supporting different functionalities (e.g., gap detection, chunking, etc.) seem unlikely to arise from different function-specific neural circuits, given the relatively uniform and self-similar wiring patterns across cortical areas and spatial scales. Additionally, reciprocal wiring patterns (with heterogeneous combinations of excitatory and inhibitory connections) in the cortex naturally speak in favor of a generic deviance detection principle. Based on this concept, we propose a network model consisting of reciprocally coupled neural masses as a blueprint of a universal change detector. Simulation examples reproduce properties of cortical deviance-related responses including the On/Off responses, the omitted-stimulus response (OSR), and the mismatch negativity (MMN). We propose that the emergence of change detectors relies on the involvement of disinhibition. An analysis of network connection settings further suggests a supportive effect of synaptic adaptation and a destructive effect of N-methyl-D-aspartate receptor (NMDA-r) antagonists on change detection. We conclude that the nature of cortical reciprocal wiring gives rise to a whole range of local change detectors supporting the notion of a generic deviance detection principle. Several testable predictions are provided based on the network model. Notably, we predict that the NMDA-r antagonists would generally dampen the cortical Off response, the cortical OSR, and the MMN.

Spectral tuning of adaptation supports coding of sensory context in auditory cortex

Perception of vocalizations and other behaviorally relevant sounds requires integrating acoustic information over hundreds of milliseconds. Sound-evoked activity in auditory cortex typically has much shorter latency, but the acoustic context, i.e., sound history, can modulate sound evoked activity over longer periods. Contextual effects are attributed to modulatory phenomena, such as stimulus-specific adaption and contrast gain control. However, an encoding model that links context to natural sound processing has yet to be established. We tested whether a model in which spectrally tuned inputs undergo adaptation mimicking short-term synaptic plasticity (STP) can account for contextual effects during natural sound processing. Single-unit activity was recorded from primary auditory cortex of awake ferrets during presentation of noise with natural temporal dynamics and fully natural sounds. Encoding properties were characterized by a standard linear-nonlinear spectro-temporal receptive field (LN) model and variants that incorporated STP-like adaptation. In the adapting models, STP was applied either globally across all input spectral channels or locally to subsets of channels. For most neurons, models incorporating local STP predicted neural activity as well or better than LN and global STP models. The strength of nonlinear adaptation varied across neurons. Within neurons, adaptation was generally stronger for spectral channels with excitatory than inhibitory gain. Neurons showing improved STP model performance also tended to undergo stimulus-specific adaptation, suggesting a common mechanism for these phenomena. When STP models were compared between passive and active behavior conditions, response gain often changed, but average STP parameters were stable. Thus, spectrally and temporally heterogeneous adaptation, subserved by a mechanism with STP-like dynamics, may support representation of the complex spectro-temporal patterns that comprise natural sounds across wide-ranging sensory contexts.

Adaptive Efficient Coding of Correlated Acoustic Properties

Natural sounds such as vocalizations often have covarying acoustic attributes, resulting in redundancy in neural coding. The efficient coding hypothesis proposes that sensory systems are able to detect such covariation and adapt to reduce redundancy, leading to more efficient neural coding. Recent psychoacoustic studies have shown the auditory system can rapidly adapt to efficiently encode two covarying dimensions as a single dimension, following passive exposure to sounds in which temporal and spectral attributes covaried in a correlated fashion. However, these studies observed a cost to this adaptation, which was a loss of sensitivity to the orthogonal dimension. Here we explore the neural basis of this psychophysical phenomenon by recording single-unit responses from the primary auditory cortex in awake ferrets exposed passively to stimuli with two correlated attributes, similar in stimulus design to the psychoacoustic experiments in humans. We found: (1) the signal-to-noise ratio of spike-rate coding of cortical responses driven by sounds with correlated attributes remained unchanged along the exposure dimension, but was reduced along the orthogonal dimension; (2) performance of a decoder trained with spike data to discriminate stimuli along the orthogonal dimension was equally reduced; (3) correlations between neurons tuned to the two covarying attributes decreased after exposure; and (4) these exposure effects still occurred if sounds were correlated along two acoustic dimensions, but varied randomly along a third dimension. These neurophysiological results are consistent with the efficient coding hypothesis and may help deepen our understanding of how the auditory system encodes and represents acoustic regularities and covariance.SIGNIFICANCE STATEMENT The efficient coding (EC) hypothesis (Attneave, 1954; Barlow, 1961) proposes that the neural code in sensory systems efficiently encodes natural stimuli by minimizing the number of spikes to transmit a sensory signal. Results of recent psychoacoustic studies in humans are consistent with the EC hypothesis in that, following passive exposure to stimuli with correlated attributes, the auditory system rapidly adapts so as to more efficiently encode the two covarying dimensions as a single dimension. In the current neurophysiological experiments, using a similar stimulus design and the experimental paradigm to the psychoacoustic studies of Stilp et al. (2010) and Stilp and Kluender (2011, 2012, 2016), we recorded responses from single neurons in the auditory cortex of the awake ferret, showing adaptive efficient neural coding of two correlated acoustic attributes.

Coding Principles in Adaptation

Adaptation is a common principle that recurs throughout the nervous system at all stages of processing. This principle manifests in a variety of phenomena, from spike frequency adaptation, to apparent changes in receptive fields with changes in stimulus statistics, to enhanced responses to unexpected stimuli. The ubiquity of adaptation leads naturally to the question: What purpose do these different types of adaptation serve? A diverse set of theories, often highly overlapping, has been proposed to explain the functional role of adaptive phenomena. In this review, we discuss several of these theoretical frameworks, highlighting relationships among them and clarifying distinctions. We summarize observations of the varied manifestations of adaptation, particularly as they relate to these theoretical frameworks, focusing throughout on the visual system and making connections to other sensory systems.

The Encoding of Speech Sounds in the Superior Temporal Gyrus

The human superior temporal gyrus (STG) is critical for extracting meaningful linguistic features from speech input. Local neural populations are tuned to acoustic-phonetic features of all consonants and vowels and to dynamic cues for intonational pitch. These populations are embedded throughout broader functional zones that are sensitive to amplitude-based temporal cues. Beyond speech features, STG representations are strongly modulated by learned knowledge and perceptual goals. Currently, a major challenge is to understand how these features are integrated across space and time in the brain during natural speech comprehension. We present a theory that temporally recurrent connections within STG generate context-dependent phonological representations, spanning longer temporal sequences relevant for coherent percepts of syllables, words, and phrases.

Capacities and neural mechanisms for auditory statistical learning across species

Statistical learning has been proposed as a possible mechanism by which individuals can become sensitive to the structures of language fundamental for speech perception. Since its description in human infants, statistical learning has been described in human adults and several non-human species as a general process by which animals learn about stimulus-relevant statistics. The neurobiology of statistical learning is beginning to be understood, but many questions remain about the underlying mechanisms. Why is the developing brain particularly sensitive to stimulus and environmental statistics, and what neural processes are engaged in the adult brain to enable learning from statistical regularities in the absence of external reward or instruction? This review will survey the statistical learning abilities of humans and non-human animals with a particular focus on communicative vocalizations. We discuss the neurobiological basis of statistical learning, and specifically what can be learned by exploring this process in both humans and laboratory animals. Finally, we describe advantages of studying vocal communication in rodents as a means to further our understanding of the cortical plasticity mechanisms engaged during statistical learning. We examine the use of rodents in the context of pup retrieval, which is an auditory-based and experience-dependent form of maternal behavior.

Brain signatures of a multiscale process of sequence learning in humans

Extracting the temporal structure of sequences of events is crucial for perception, decision-making, and language processing. Here, we investigate the mechanisms by which the brain acquires knowledge of sequences and the possibility that successive brain responses reflect the progressive extraction of sequence statistics at different timescales. We measured brain activity using magnetoencephalography in humans exposed to auditory sequences with various statistical regularities, and we modeled this activity as theoretical surprise levels using several learning models. Successive brain waves related to different types of statistical inferences. Early post-stimulus brain waves denoted a sensitivity to a simple statistic, the frequency of items estimated over a long timescale (habituation). Mid-latency and late brain waves conformed qualitatively and quantitatively to the computational properties of a more complex inference: the learning of recent transition probabilities. Our findings thus support the existence of multiple computational systems for sequence processing involving statistical inferences at multiple scales.

Spike-timing-dependent ensemble encoding by non-classically responsive cortical neurons

Neurons recorded in behaving animals often do not discernibly respond to sensory input and are not overtly task-modulated. These non-classically responsive neurons are difficult to interpret and are typically neglected from analysis, confounding attempts to connect neural activity to perception and behavior. Here, we describe a trial-by-trial, spike-timing-based algorithm to reveal the coding capacities of these neurons in auditory and frontal cortex of behaving rats. Classically responsive and non-classically responsive cells contained significant information about sensory stimuli and behavioral decisions. Stimulus category was more accurately represented in frontal cortex than auditory cortex, via ensembles of non-classically responsive cells coordinating the behavioral meaning of spike timings on correct but not error trials. This unbiased approach allows the contribution of all recorded neurons – particularly those without obvious task-related, trial-averaged firing rate modulation – to be assessed for behavioral relevance on single trials.

Neurons encode information in the form of electrical signals called spikes. Certain neurons increase the rate at which they produce spikes under specific circumstances, e.g., whenever an animal hears a particular sound. These neurons are said to be 'classically responsive'. But not all neurons behave in this way. Others produce spikes at a variable rate that does not obviously relate to the animal's behavior. These neurons are said to be 'non-classically responsive'. They are often omitted from analyses, despite typically outnumbering their classically responsive counterparts. So, what are these neurons doing?

To find out, Insanally et al. trained rats to respond to sounds. The animals learned to poke their nose into a window whenever they heard a specific tone, and to avoid responding whenever they heard any other tone. As the rats performed the task, Insanally et al. recorded from neurons in two areas of the brain, the frontal cortex and the auditory cortex. A computer then analyzed the activity of individual neurons during each trial.

As expected, the firing rate of non-classically responsive cells did not relate to the animals' behavior. But the timing of this firing did. The interval between spikes contained information about which tone the animals had heard and/or how they had responded. The cells worked together in groups to encode this information. Over the course of each trial, every neuron in the group varied the interval between its spikes. Eventually, the group reached a consensus, with all neurons using the same interval to represent information relevant to the task. Groups of neurons in the frontal cortex encoded more information about the category of the tone than those in the auditory cortex.

By including all neurons – both classically and non-classically responsive – this model offers a more comprehensive view of how neural activity relates to behavior. This may in turn help us understand the variable and complex neural activity seen in people with sensory and cognitive disorders.

Enhanced deviant responses in patterned relative to random sound sequences

The brain draws on knowledge of statistical structure in the environment to facilitate detection of new events. Understanding the nature of this representation is a key challenge in sensory neuroscience. Specifically, it is unknown whether real-time perception of rapidly-unfolding sensory signals is driven by a coarse or detailed representation of the proximal stimulus history. We recorded electroencephalography brain responses to frequency outliers in regularly-patterned (REG) versus random (RAND) tone-pip sequences which were generated anew on each trial. REG and RAND sequences were matched in frequency content and span, only differing in the specific order of the tone-pips. Stimuli were very rapid, limiting conscious reasoning in favour of automatic processing of regularity. Listeners were naïve and performed an incidental visual task. Outliers within REG evoked a larger response than matched outliers in RAND. These effects arose rapidly (within 80 msec) and were underpinned by distinct sources from those classically associated with frequency-based deviance detection. These findings are consistent with the notion that the brain continually maintains a detailed representation of ongoing sensory input and that this representation shapes the processing of incoming information. Predominantly auditory-cortical sources code for frequency deviance whilst frontal sources are associated with tracking more complex sequence structure.

Neural Correlate of Transition Violation and Deviance Detection in the Songbird Auditory Forebrain

Deviants are stimuli that violate one's prediction about the incoming stimuli. Studying deviance detection helps us understand how nervous system learns temporal patterns between stimuli and forms prediction about the future. Detecting deviant stimuli is also critical for animals' survival in the natural environment filled with complex sounds and patterns. Using natural songbird vocalizations as stimuli, we recorded multi-unit and single-unit activity from the zebra finch auditory forebrain while presenting rare repeated stimuli after regular alternating stimuli (alternating oddball experiment) or rare deviant among multiple different common stimuli (context oddball experiment). The alternating oddball experiment showed that neurons were sensitive to rare repetitions in regular alternations. In the absence of expectation, repetition suppresses neural responses to the 2nd stimulus in the repetition. When repetition violates expectation, neural responses to the 2nd stimulus in the repetition were stronger than expected. The context oddball experiment showed that a stimulus elicits stronger neural responses when it is presented infrequently as a deviant among multiple common stimuli. As the acoustic differences between deviant and common stimuli increase, the response enhancement also increases. These results together showed that neural encoding of a stimulus depends not only on the acoustic features of the stimulus but also on the preceding stimuli and the transition patterns between them. These results also imply that the classical oddball effect may result from a combination of repetition suppression and deviance enhancement. Classification analyses showed that the difficulties in decoding the stimulus responsible for the neural responses differed for deviants in different experimental conditions. These findings suggest that learning transition patterns and detecting deviants in natural sequences may depend on a hierarchy of neural mechanisms, which may be involved in more complex forms of auditory processing that depend on the transition patterns between stimuli, such as speech processing.

Recent advances in understanding the auditory cortex

Our ability to make sense of the auditory world results from neural processing that begins in the ear, goes through multiple subcortical areas, and continues in the cortex. The specific contribution of the auditory cortex to this chain of processing is far from understood. Although many of the properties of neurons in the auditory cortex resemble those of subcortical neurons, they show somewhat more complex selectivity for sound features, which is likely to be important for the analysis of natural sounds, such as speech, in real-life listening conditions. Furthermore, recent work has shown that auditory cortical processing is highly context-dependent, integrates auditory inputs with other sensory and motor signals, depends on experience, and is shaped by cognitive demands, such as attention. Thus, in addition to being the locus for more complex sound selectivity, the auditory cortex is increasingly understood to be an integral part of the network of brain regions responsible for prediction, auditory perceptual decision-making, and learning. In this review, we focus on three key areas that are contributing to this understanding: the sound features that are preferentially represented by cortical neurons, the spatial organization of those preferences, and the cognitive roles of the auditory cortex.

Dimension-selective attention as a possible driver of dynamic, context-dependent re-weighting in speech processing

The contribution of acoustic dimensions to an auditory percept is dynamically adjusted and reweighted based on prior experience about how informative these dimensions are across the long-term and short-term environment. This is especially evident in speech perception, where listeners differentially weight information across multiple acoustic dimensions, and use this information selectively to update expectations about future sounds. The dynamic and selective adjustment of how acoustic input dimensions contribute to perception has made it tempting to conceive of this as a form of non-spatial auditory selective attention. Here, we review several human speech perception phenomena that might be consistent with auditory selective attention although, as of yet, the literature does not definitively support a mechanistic tie. We relate these human perceptual phenomena to illustrative nonhuman animal neurobiological findings that offer informative guideposts in how to test mechanistic connections. We next present a novel empirical approach that can serve as a methodological bridge from human research to animal neurobiological studies. Finally, we describe four preliminary results that demonstrate its utility in advancing understanding of human non-spatial dimension-based auditory selective attention.

Beyond Trial-Based Paradigms: Continuous Behavior, Ongoing Neural Activity, and Natural Stimuli

The vast majority of experiments examining perception and behavior are conducted using experimental paradigms that adhere to a rigid trial structure: each trial consists of a brief and discrete series of events and is regarded as independent from all other trials. The assumptions underlying this structure ignore the reality that natural behavior is rarely discrete, brain activity follows multiple time courses that do not necessarily conform to the trial structure, and the natural environment has statistical structure and dynamics that exhibit long-range temporal correlation. Modern advances in statistical modeling and analysis offer tools that make it feasible for experiments to move beyond rigid independent and identically distributed trial structures. Here we review literature that serves as evidence for the feasibility and advantages of moving beyond trial-based paradigms to understand the neural basis of perception and cognition. Furthermore, we propose a synthesis of these efforts, integrating the characterization of natural stimulus properties with measurements of continuous neural activity and behavioral outputs within the framework of sensory-cognitive-motor loops. Such a framework provides a basis for the study of natural statistics, naturalistic tasks, and/or slow fluctuations in brain activity, which should provide starting points for important generalizations of analytical tools in neuroscience and subsequent progress in understanding the neural basis of perception and cognition.

Deviance sensitivity in the auditory cortex of freely moving rats

Deviance sensitivity is the specific response to a surprising stimulus, one that violates expectations set by the past stimulation stream. In audition, deviance sensitivity is often conflated with stimulus-specific adaptation (SSA), the decrease in responses to a common stimulus that only partially generalizes to other, rare stimuli. SSA is usually measured using oddball sequences, where a common (standard) tone and a rare (deviant) tone are randomly intermixed. However, the larger responses to a tone when deviant does not necessarily represent deviance sensitivity. Deviance sensitivity is commonly tested using a control sequence in which many different tones serve as the standard, eliminating the expectations set by the standard ('deviant among many standards'). When the response to a tone when deviant (against a single standard) is larger than the responses to the same tone in the control sequence, it is concluded that true deviance sensitivity occurs. In primary auditory cortex of anesthetized rats, responses to deviants and to the same tones in the control condition are comparable in size. We recorded local field potentials and multiunit activity from the auditory cortex of awake, freely moving rats, implanted with 32-channel drivable microelectrode arrays and using telemetry. We observed highly significant SSA in the awake state. Moreover, the responses to a tone when deviant were significantly larger than the responses to the same tone in the control condition. These results establish the presence of true deviance sensitivity in primary auditory cortex in awake rats.

Saliency Detection as a Reactive Process: Unexpected Sensory Events Evoke Corticomuscular Coupling

Survival in a fast-changing environment requires animals not only to detect unexpected sensory events, but also to react. In humans, these salient sensory events generate large electrocortical responses, which have been traditionally interpreted within the sensory domain. Here we describe a basic physiological mechanism coupling saliency-related cortical responses with motor output. In four experiments conducted on 70 healthy participants, we show that salient substartle sensory stimuli modulate isometric force exertion by human participants, and that this modulation is tightly coupled with electrocortical activity elicited by the same stimuli. We obtained four main results. First, the force modulation follows a complex triphasic pattern consisting of alternating decreases and increases of force, time-locked to stimulus onset. Second, this modulation occurs regardless of the sensory modality of the eliciting stimulus. Third, the magnitude of the force modulation is predicted by the amplitude of the electrocortical activity elicited by the same stimuli. Fourth, both neural and motor effects are not reflexive but depend on contextual factors. Together, these results indicate that sudden environmental stimuli have an immediate effect on motor processing, through a tight corticomuscular coupling. These observations suggest that saliency detection is not merely perceptive but reactive, preparing the animal for subsequent appropriate actions.SIGNIFICANCE STATEMENT Salient events occurring in the environment, regardless of their modalities, elicit large electrical brain responses, dominated by a widespread "vertex" negative-positive potential. This response is the largest synchronization of neural activity that can be recorded from a healthy human being. Current interpretations assume that this vertex potential reflects sensory processes. Contrary to this general assumption, we show that the vertex potential is strongly coupled with a modulation of muscular activity that follows the same pattern. Both the vertex potential and its motor effects are not reflexive but strongly depend on contextual factors. These results reconceptualize the significance of these evoked electrocortical responses, suggesting that saliency detection is not merely perceptive but reactive, preparing the animal for subsequent appropriate actions.

Neural Integration of Stimulus History Underlies Prediction for Naturalistically Evolving Sequences

Forming valid predictions about the environment is crucial to survival. However, whether humans are able to form valid predictions about natural stimuli based on their temporal statistical regularities remains unknown. Here, we presented subjects with tone sequences with pitch fluctuations that, over time, capture long-range temporal dependence structures prevalent in natural stimuli. We found that subjects were able to exploit such naturalistic statistical regularities to make valid predictions about upcoming items in a sequence. Magnetoencephalography (MEG) recordings revealed that slow, arrhythmic cortical dynamics tracked the evolving pitch sequence over time such that neural activity at a given moment was influenced by the pitch of up to seven previous tones. Importantly, such history integration contained in neural activity predicted the expected pitch of the upcoming tone, providing a concrete computational mechanism for prediction. These results establish humans' ability to make valid predictions based on temporal regularities inherent in naturalistic stimuli and further reveal the neural mechanisms underlying such predictive computation.SIGNIFICANCE STATEMENT A fundamental question in neuroscience is how the brain predicts upcoming events in the environment. To date, this question has primarily been addressed in experiments using relatively simple stimulus sequences. Here, we studied predictive processing in the human brain using auditory tone sequences that exhibit temporal statistical regularities similar to those found in natural stimuli. We observed that humans are able to form valid predictions based on such complex temporal statistical regularities. We further show that neural response to a given tone in the sequence reflects integration over the preceding tone sequence and that this history dependence forms the foundation for prediction. These findings deepen our understanding of how humans form predictions in an ecologically valid environment.

Predictive cues for auditory stream formation in humans and monkeys

Auditory perception is improved when stimuli are predictable, and this effect is evident in a modulation of the activity of neurons in the auditory cortex as shown previously. Human listeners can better predict the presence of duration deviants embedded in stimulus streams with fixed interonset interval (isochrony) and repeated duration pattern (regularity), and neurons in the auditory cortex of macaque monkeys have stronger sustained responses in the 60-140 ms post-stimulus time window under these conditions. Subsequently, the question has arisen whether isochrony or regularity in the sensory input contributed to the enhancement of the neuronal and behavioural responses. Therefore, we varied the two factors isochrony and regularity independently and measured the ability of human subjects to detect deviants embedded in these sequences as well as measuring the responses of neurons the primary auditory cortex of macaque monkeys during presentations of the sequences. The performance of humans in detecting deviants was significantly increased by regularity. Isochrony enhanced detection only in the presence of the regularity cue. In monkeys, regularity increased the sustained component of neuronal tone responses in auditory cortex while isochrony had no consistent effect. Although both regularity and isochrony can be considered as parameters that would make a sequence of sounds more predictable, our results from the human and monkey experiments converge in that regularity has a greater influence on behavioural performance and neuronal responses.

Sensory Stream Adaptation in Chaotic Networks

Implicit expectations induced by predictable stimuli sequences affect neuronal response to upcoming stimuli at both single cell and neural population levels. Temporally regular sensory streams also phase entrain ongoing low frequency brain oscillations but how and why this happens is unknown. Here we investigate how random recurrent neural networks without plasticity respond to stimuli streams containing oddballs. We found the neuronal correlates of sensory stream adaptation emerge if networks generate chaotic oscillations which can be phase entrained by stimulus streams. The resultant activity patterns are close to critical and support history dependent response on long timescales. Because critical network entrainment is a slow process stimulus response adapts gradually over multiple repetitions. Repeated stimuli generate suppressed responses but oddball responses are large and distinct. Oscillatory mismatch responses persist in population activity for long periods after stimulus offset while individual cell mismatch responses are strongly phasic. These effects are weakened in temporally irregular sensory streams. Thus we show that network phase entrainment provides a biologically plausible mechanism for neural oddball detection. Our results do not depend on specific network characteristics, are consistent with experimental studies and may be relevant for multiple pathologies demonstrating altered mismatch processing such as schizophrenia and depression.

Familiar But Unexpected: Effects of Sound Context Statistics on Auditory Responses in the Songbird Forebrain

Rapid discrimination of salient acoustic signals in the noisy natural environment may depend, not only on specific stimulus features, but also on previous experience that generates expectations about upcoming events. We studied the neural correlates of expectation in the songbird forebrain by using natural vocalizations as stimuli and manipulating the category and familiarity of context sounds. In our paradigm, we recorded bilaterally from auditory neurons in awake adult male zebra finches with multiple microelectrodes during repeated playback of a conspecific song, followed by further playback of this test song in different interleaved sequences with other conspecific or heterospecific songs. Significant enhancement in the auditory response to the test song was seen when its acoustic features differed from the statistical distribution of context song features, but not when it shared the same distribution. Enhancement was also seen when the time of occurrence of the test song was uncertain. These results show that auditory forebrain responses in awake animals in the passive hearing state are modulated dynamically by previous auditory experience and imply that the auditory system can identify the category of a sound based on the global features of the acoustic context. Furthermore, this probability-dependent enhancement in responses to surprising stimuli is independent of stimulus-specific adaptation, which tracks familiarity, suggesting that the two processes could coexist in auditory processing. These findings establish the songbird as a model system for studying these phenomena and contribute to our understanding of statistical learning and the origin of human ERP phenomena to unexpected stimuli.SIGNIFICANCE STATEMENT Traditional auditory neurophysiology has mapped acoustic features of sounds to the response properties of neurons; however, growing evidence suggests that neurons can also encode the probability of sounds. We recorded responses of songbird auditory neurons in a novel paradigm that presented a familiar test stimulus in a sequence with similar or dissimilar sounds. The responses encode, not only stimulus familiarity, but also the expectation for a class of sounds based on the recent statistics of varying sounds in the acoustic context. Our approach thus provides a model system that uses a controlled stimulus paradigm to understand the mechanisms by which top-down processes (expectation and memory) and bottom-up processes (based on stimulus features) interact in sensory coding.

Detection of Tones Masked by Fluctuating Noise in Rat Auditory Cortex

Sounds in natural settings always appear over a noisy background. The masked threshold of a pure tone in white noise (the lowest sound level at which the tone can be detected in the presence of masking noise) is largely determined by energy masking in the peripheral auditory system: when the signal-to-noise ratio within a frequency band centered at the target tone frequency is large enough, the tone can be detected. However, when additional information is supplied to the auditory system, for example in the presence of slow and coherent modulations of a broadband masker (often found in natural sounds), masked thresholds can be reduced substantially below the values expected from pure energy masking. Here, we used intracellular recordings in vivo in rat auditory cortex in order to study neuronal responses to pure tones masked by broadband maskers and amplitude-modulated broadband maskers. When tones were embedded in amplitude-modulated noise, detection thresholds were substantially lower than when embedded in unmodulated noise. The main cue for tone detection in modulated noise consisted of the suppression of the locking of the neuronal responses to the amplitude modulation of the noise by low-level tones.