Sensitivity to complex statistical regularities in rat auditory cortex

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.

Great Expectations: Is there Evidence for Predictive Coding in Auditory Cortex?

Predictive coding is possibly one of the most influential, comprehensive, and controversial theories of neural function. While proponents praise its explanatory potential, critics object that key tenets of the theory are untested or even untestable. The present article critically examines existing evidence for predictive coding in the auditory modality. Specifically, we identify five key assumptions of the theory and evaluate each in the light of animal, human and modeling studies of auditory pattern processing. For the first two assumptions - that neural responses are shaped by expectations and that these expectations are hierarchically organized - animal and human studies provide compelling evidence. The anticipatory, predictive nature of these expectations also enjoys empirical support, especially from studies on unexpected stimulus omission. However, for the existence of separate error and prediction neurons, a key assumption of the theory, evidence is lacking. More work exists on the proposed oscillatory signatures of predictive coding, and on the relation between attention and precision. However, results on these latter two assumptions are mixed or contradictory. Looking to the future, more collaboration between human and animal studies, aided by model-based analyses will be needed to test specific assumptions and implementations of predictive coding - and, as such, help determine whether this popular grand theory can fulfill its expectations.

Comodulation Enhances Signal Detection via Priming of Auditory Cortical Circuits

Acoustic environments are composed of complex overlapping sounds that the auditory system is required to segregate into discrete perceptual objects. The functions of distinct auditory processing stations in this challenging task are poorly understood. Here we show a direct role for mouse auditory cortex in detection and segregation of acoustic information. We measured the sensitivity of auditory cortical neurons to brief tones embedded in masking noise. By altering spectrotemporal characteristics of the masker, we reveal that sensitivity to pure tone stimuli is strongly enhanced in coherently modulated broadband noise, corresponding to the psychoacoustic phenomenon comodulation masking release. Improvements in detection were largest following priming periods of noise alone, indicating that cortical segregation is enhanced over time. Transient opsin-mediated silencing of auditory cortex during the priming period almost completely abolished these improvements, suggesting that cortical processing may play a direct and significant role in detection of quiet sounds in noisy environments.

SIGNIFICANCE STATEMENT Auditory systems are adept at detecting and segregating competing sound sources, but there is little direct evidence of how this process occurs in the mammalian auditory pathway. We demonstrate that coherent broadband noise enhances signal representation in auditory cortex, and that prolonged exposure to noise is necessary to produce this enhancement. Using optogenetic perturbation to selectively silence auditory cortex during early noise processing, we show that cortical processing plays a crucial role in the segregation of competing sounds.

Responses to Predictable versus Random Temporally Complex Stimuli from Single Units in Auditory Thalamus: Impact of Aging and Anesthesia

Human aging studies suggest that an increased use of top-down knowledge-based resources would compensate for degraded upstream acoustic information to accurately identify important temporally rich signals. Sinusoidal amplitude-modulated (SAM) stimuli have been used to mimic the fast-changing temporal features in speech and species-specific vocalizations. Single units were recorded from auditory thalamus [medial geniculate body (MGB)] of young awake, aged awake, young anesthetized, and aged anesthetized rats. SAM stimuli were modulated between 2 and 1024 Hz with the modulation frequency (fm) changed randomly (RAN) across trials or sequentially (SEQ) after several repeated trials. Units were found to be RAN-preferring, SEQ-preferring, or nonselective based on total firing rate. Significant anesthesia and age effects were found. The majority (86%) of young anesthetized units preferred RAN SAM stimuli; significantly fewer young awake units (51%, p < 0.0001) preferred RAN SAM signals with 16% preferring SEQ SAM. Compared with young awake units, there was a significant increase of aged awake units preferring SEQ SAM (30%, p < 0.05). We examined RAN versus SEQ differences across fms by measuring selective fm areas under the rate modulation transfer function curve. The largest age-related differences from awake animals were found for mid-to-high fms in MGB units, with young units preferring RAN SAM while aged units showed a greater preference for SEQ-presented SAM. Together, these findings suggest that aged MGB units/animals employ increased top-down mediated stimulus context to enhance processing of "expected" temporally rich stimuli, especially at more challenging higher fms.

SIGNIFICANCE STATEMENT

Older individuals compensate for impaired ascending acoustic information by increasing use of cortical cognitive and attentional resources. The interplay between ascending and descending influences in the thalamus may serve to enhance the salience of speech signals that are degraded as they ascend to the cortex. The present findings demonstrate that medial geniculate body units from awake rats show an age-related preference for predictable modulated signals relative to randomly presented signals, especially at higher, more challenging modulation frequencies. Conversely, units from anesthetized animals, with little top-down influences, strongly preferred randomly presented modulated sequences. These results suggest a neuronal substrate for an age-related increase in experience/attentional-based influences in processing temporally complex auditory information in the auditory thalamus.

The Cumulative Effects of Predictability on Synaptic Gain in the Auditory Processing Stream

Stimulus predictability can lead to substantial modulations of brain activity, such as shifts in sustained magnetic field amplitude, measured with magnetoencephalography (MEG). Here, we provide a mechanistic explanation of these effects using MEG data acquired from healthy human volunteers (N = 13, 7 female). In a source-level analysis of induced responses, we established the effects of orthogonal predictability manipulations of rapid tone-pip sequences (namely, sequence regularity and alphabet size) along the auditory processing stream. In auditory cortex, regular sequences with smaller alphabets induced greater gamma activity. Furthermore, sequence regularity shifted induced activity in frontal regions toward higher frequencies. To model these effects in terms of the underlying neurophysiology, we used dynamic causal modeling for cross-spectral density and estimated slow fluctuations in neural (postsynaptic) gain. Using the model-based parameters, we accurately explain the sensor-level sustained field amplitude, demonstrating that slow changes in synaptic efficacy, combined with sustained sensory input, can result in profound and sustained effects on neural responses to predictable sensory streams.

SIGNIFICANCE STATEMENT Brain activity can be strongly modulated by the predictability of stimuli it is currently processing. An example of such a modulation is a shift in sustained magnetic field amplitude, measured with magnetoencephalography. Here, we provide a mechanistic explanation of these effects. First, we establish the oscillatory neural correlates of independent predictability manipulations in hierarchically distinct areas of the auditory processing stream. Next, we use a biophysically realistic computational model to explain these effects in terms of the underlying neurophysiology. Finally, using the model-based parameters describing neural gain modulation, we can explain the previously unexplained effects observed at the sensor level. This demonstrates that slow modulations of synaptic gain can result in profound and sustained effects on neural activity.

Frequency tagging to track the neural processing of contrast in fast, continuous sound sequences

The human auditory system presents a remarkable ability to detect rapid changes in fast, continuous acoustic sequences, as best illustrated in speech and music. However, the neural processing of rapid auditory contrast remains largely unclear, probably due to the lack of methods to objectively dissociate the response components specifically related to the contrast from the other components in response to the sequence of fast continuous sounds. To overcome this issue, we tested a novel use of the frequency-tagging approach allowing contrast-specific neural responses to be tracked based on their expected frequencies. The EEG was recorded while participants listened to 40-s sequences of sounds presented at 8Hz. A tone or interaural time contrast was embedded every fifth sound (AAAAB), such that a response observed in the EEG at exactly 8 Hz/5 (1.6 Hz) or harmonics should be the signature of contrast processing by neural populations. Contrast-related responses were successfully identified, even in the case of very fine contrasts. Moreover, analysis of the time course of the responses revealed a stable amplitude over repetitions of the AAAAB patterns in the sequence, except for the response to perceptually salient contrasts that showed a buildup and decay across repetitions of the sounds. Overall, this new combination of frequency-tagging with an oddball design provides a valuable complement to the classic, transient, evoked potentials approach, especially in the context of rapid auditory information. Specifically, we provide objective evidence on the neural processing of contrast embedded in fast, continuous sound sequences.NEW & NOTEWORTHY Recent theories suggest that the basis of neurodevelopmental auditory disorders such as dyslexia might be an impaired processing of fast auditory changes, highlighting how the encoding of rapid acoustic information is critical for auditory communication. Here, we present a novel electrophysiological approach to capture in humans neural markers of contrasts in fast continuous tone sequences. Contrast-specific responses were successfully identified, even for very fine contrasts, providing direct insight on the encoding of rapid auditory information.

Sequence learning modulates neural responses and oscillatory coupling in human and monkey auditory cortex

Learning complex ordering relationships between sensory events in a sequence is fundamental for animal perception and human communication. While it is known that rhythmic sensory events can entrain brain oscillations at different frequencies, how learning and prior experience with sequencing relationships affect neocortical oscillations and neuronal responses is poorly understood. We used an implicit sequence learning paradigm (an "artificial grammar") in which humans and monkeys were exposed to sequences of nonsense words with regularities in the ordering relationships between the words. We then recorded neural responses directly from the auditory cortex in both species in response to novel legal sequences or ones violating specific ordering relationships. Neural oscillations in both monkeys and humans in response to the nonsense word sequences show strikingly similar hierarchically nested low-frequency phase and high-gamma amplitude coupling, establishing this form of oscillatory coupling-previously associated with speech processing in the human auditory cortex-as an evolutionarily conserved biological process. Moreover, learned ordering relationships modulate the observed form of neural oscillatory coupling in both species, with temporally distinct neural oscillatory effects that appear to coordinate neuronal responses in the monkeys. This study identifies the conserved auditory cortical neural signatures involved in monitoring learned sequencing operations, evident as modulations of transient coupling and neuronal responses to temporally structured sensory input.

Stimulus-specific adaptation in a recurrent network model of primary auditory cortex

Stimulus-specific adaptation (SSA) occurs when neurons decrease their responses to frequently-presented (standard) stimuli but not, or not as much, to other, rare (deviant) stimuli. SSA is present in all mammalian species in which it has been tested as well as in birds. SSA confers short-term memory to neuronal responses, and may lie upstream of the generation of mismatch negativity (MMN), an important human event-related potential. Previously published models of SSA mostly rely on synaptic depression of the feedforward, thalamocortical input. Here we study SSA in a recurrent neural network model of primary auditory cortex. When the recurrent, intracortical synapses display synaptic depression, the network generates population spikes (PSs). SSA occurs in this network when deviants elicit a PS but standards do not, and we demarcate the regions in parameter space that allow SSA. While SSA based on PSs does not require feedforward depression, we identify feedforward depression as a mechanism for expanding the range of parameters that support SSA. We provide predictions for experiments that could help differentiate between SSA due to synaptic depression of feedforward connections and SSA due to synaptic depression of recurrent connections. Similar to experimental data, the magnitude of SSA in the model depends on the frequency difference between deviant and standard, probability of the deviant, inter-stimulus interval and input amplitude. In contrast to models based on feedforward depression, our model shows true deviance sensitivity as found in experiments.

Modelling auditory attention

Sounds in everyday life seldom appear in isolation. Both humans and machines are constantly flooded with a cacophony of sounds that need to be sorted through and scoured for relevant information-a phenomenon referred to as the 'cocktail party problem'. A key component in parsing acoustic scenes is the role of attention, which mediates perception and behaviour by focusing both sensory and cognitive resources on pertinent information in the stimulus space. The current article provides a review of modelling studies of auditory attention. The review highlights how the term attention refers to a multitude of behavioural and cognitive processes that can shape sensory processing. Attention can be modulated by 'bottom-up' sensory-driven factors, as well as 'top-down' task-specific goals, expectations and learned schemas. Essentially, it acts as a selection process or processes that focus both sensory and cognitive resources on the most relevant events in the soundscape; with relevance being dictated by the stimulus itself (e.g. a loud explosion) or by a task at hand (e.g. listen to announcements in a busy airport). Recent computational models of auditory attention provide key insights into its role in facilitating perception in cluttered auditory scenes.This article is part of the themed issue 'Auditory and visual scene analysis'.

Human Inferences about Sequences: A Minimal Transition Probability Model

The brain constantly infers the causes of the inputs it receives and uses these inferences to generate statistical expectations about future observations. Experimental evidence for these expectations and their violations include explicit reports, sequential effects on reaction times, and mismatch or surprise signals recorded in electrophysiology and functional MRI. Here, we explore the hypothesis that the brain acts as a near-optimal inference device that constantly attempts to infer the time-varying matrix of transition probabilities between the stimuli it receives, even when those stimuli are in fact fully unpredictable. This parsimonious Bayesian model, with a single free parameter, accounts for a broad range of findings on surprise signals, sequential effects and the perception of randomness. Notably, it explains the pervasive asymmetry between repetitions and alternations encountered in those studies. Our analysis suggests that a neural machinery for inferring transition probabilities lies at the core of human sequence knowledge.

Passive exposure to speech sounds induces long-term memory representations in the auditory cortex of adult rats

Experience-induced changes in the functioning of the auditory cortex are prominent in early life, especially during a critical period. Although auditory perceptual learning takes place automatically during this critical period, it is thought to require active training in later life. Previous studies demonstrated rapid changes in single-cell responses of anesthetized adult animals while exposed to sounds presented in a statistical learning paradigm. However, whether passive exposure to sounds can form long-term memory representations remains to be demonstrated. To investigate this issue, we first exposed adult rats to human speech sounds for 3 consecutive days, 12 h/d. Two groups of rats exposed to either spectrotemporal or tonal changes in speech sounds served as controls for each other. Then, electrophysiological brain responses from the auditory cortex were recorded to the same stimuli. In both the exposure and test phase statistical learning paradigm, was applied. The exposure effect was found for the spectrotemporal sounds, but not for the tonal sounds. Only the animals exposed to spectrotemporal sounds differentiated subtle changes in these stimuli as indexed by the mismatch negativity response. The results point to the occurrence of long-term memory traces for the speech sounds due to passive exposure in adult animals.

Rapid Sensory Adaptation Redux: A Circuit Perspective

Adaptation is fundamental to life. All organisms adapt over timescales that span from evolution to generations and lifetimes to moment-by-moment interactions. The nervous system is particularly adept at rapidly adapting to change, and this in fact may be one of its fundamental principles of organization and function. Rapid forms of sensory adaptation have been well documented across all sensory modalities in a wide range of organisms, yet we do not have a comprehensive understanding of the adaptive cellular mechanisms that ultimately give rise to the corresponding percepts, due in part to the complexity of the circuitry. In this Perspective, we aim to build links between adaptation at multiple scales of neural circuitry by investigating the differential adaptation across brain regions and sub-regions and across specific cell types, for which the explosion of modern tools has just begun to enable. This investigation points to a set of challenges for the field to link functional observations to adaptive properties of the neural circuit that ultimately underlie percepts.

The Representation of Prediction Error in Auditory Cortex

To survive, organisms must extract information from the past that is relevant for their future. How this process is expressed at the neural level remains unclear. We address this problem by developing a novel approach from first principles. We show here how to generate low-complexity representations of the past that produce optimal predictions of future events. We then illustrate this framework by studying the coding of ‘oddball’ sequences in auditory cortex. We find that for many neurons in primary auditory cortex, trial-by-trial fluctuations of neuronal responses correlate with the theoretical prediction error calculated from the short-term past of the stimulation sequence, under constraints on the complexity of the representation of this past sequence. In some neurons, the effect of prediction error accounted for more than 50% of response variability. Reliable predictions often depended on a representation of the sequence of the last ten or more stimuli, although the representation kept only few details of that sequence.

A crucial aspect of all life is the ability to use past events in order to guide future behavior. To do that, creatures need the ability to predict future events. Indeed, predictability has been shown to affect neuronal responses in many animals and under many conditions. Clearly, the quality of predictions should depend on the amount and detail of the past information used to generate them. Here, by using a basic principle from information theory, we show how to derive explicitly the tradeoff between quality of prediction and complexity of the representation of past information. We then apply these ideas to a concrete case–neuronal responses recorded in auditory cortex during the presentation of oddball sequences, consisting of two tones with varying probabilities. We show that the neuronal responses fit quantitatively the prediction errors of optimal predictors derived from our theory, and use that result in order to deduce the properties of the representations of the past in the auditory system. We conclude that these memory representations have surprisingly long duration (10 stimuli back or more), but keep relatively little detail about this past. Our theory can be applied widely to other sensory systems.

Hierarchy of prediction errors for auditory events in human temporal and frontal cortex

Predictive coding theories posit that neural networks learn statistical regularities in the environment for comparison with actual outcomes, signaling a prediction error (PE) when sensory deviation occurs. PE studies in audition have capitalized on low-frequency event-related potentials (LF-ERPs), such as the mismatch negativity. However, local cortical activity is well-indexed by higher-frequency bands [high-γ band (Hγ): 80-150 Hz]. We compared patterns of human Hγ and LF-ERPs in deviance detection using electrocorticographic recordings from subdural electrodes over frontal and temporal cortices. Patients listened to trains of task-irrelevant tones in two conditions differing in the predictability of a deviation from repetitive background stimuli (fully predictable vs. unpredictable deviants). We found deviance-related responses in both frequency bands over lateral temporal and inferior frontal cortex, with an earlier latency for Hγ than for LF-ERPs. Critically, frontal Hγ activity but not LF-ERPs discriminated between fully predictable and unpredictable changes, with frontal cortex sensitive to unpredictable events. The results highlight the role of frontal cortex and Hγ activity in deviance detection and PE generation.

Attentional and Contextual Priors in Sound Perception

Behavioral and neural studies of selective attention have consistently demonstrated that explicit attentional cues to particular perceptual features profoundly alter perception and performance. The statistics of the sensory environment can also provide cues about what perceptual features to expect, but the extent to which these more implicit contextual cues impact perception and performance, as well as their relationship to explicit attentional cues, is not well understood. In this study, the explicit cues, or attentional prior probabilities, and the implicit cues, or contextual prior probabilities, associated with different acoustic frequencies in a detection task were simultaneously manipulated. Both attentional and contextual priors had similarly large but independent impacts on sound detectability, with evidence that listeners tracked and used contextual priors for a variety of sound classes (pure tones, harmonic complexes, and vowels). Further analyses showed that listeners updated their contextual priors rapidly and optimally, given the changing acoustic frequency statistics inherent in the paradigm. A Bayesian Observer model accounted for both attentional and contextual adaptations found with listeners. These results bolster the interpretation of perception as Bayesian inference, and suggest that some effects attributed to selective attention may be a special case of contextual prior integration along a feature axis.

Processing Timescales as an Organizing Principle for Primate Cortex

An emerging view posits a timescale-based cortical topography, with integration windows increasing from sensory to association areas. In this issue, Chaudhuri et al. (2015) present a cortical model wherein a hierarchy of timescales arises from local and inter-regional circuit dynamics.

Facilitative effect of repetitive presentation of one stimulus on cortical responses to other stimuli in macaque monkeys--a possible neural mechanism for mismatch negativity

The event-related potential 'mismatch negativity' (MMN) is an indicator of a perceiver's ability to detect deviations in sensory signal streams. MMN and its homologue in animals, mismatch activity (MMA), are differential neural responses to a repeatedly presented stimulus and a subsequent deviant stimulus (oddball). Because neural mechanisms underlying MMN and MMA remain unclear, there is a controversy as to whether MMN and MMA arise solely from stimulus-specific adaptation (SSA), in which the response to a stimulus cumulatively attenuates with its repetitive presentation. To address this issue, we used electrocorticography and the auditory roving-oddball paradigm in two awake macaque monkeys. We examined the effect of stimulus repetition number on MMA and on responses to repeated stimuli and oddballs across the cerebral cortex in the time-frequency domain. As the repetition number increased, MMA spread across the temporal, frontal and parietal cortices, and each electrode yielded a larger MMA. Surprisingly, this increment in MMA largely depended on response augmentation to the oddball rather than on SSA to the repeated stimulus. Following sufficient repetition, the oddball evoked a spectral power increment in some electrodes on the frontal cortex that had shown no power increase to the stimuli with less or no preceding repetition. We thereby revealed that repetitive presentation of one stimulus not only leads to SSA but also facilitates the cortical response to oddballs involving a wide range of cortical regions. This facilitative effect might underlie the generation of MMN-like scalp potentials in macaques that potentially shares similar neural mechanisms with MMN in humans.

Central Gain Restores Auditory Processing following Near-Complete Cochlear Denervation

Sensory organ damage induces a host of cellular and physiological changes in the periphery and the brain. Here, we show that some aspects of auditory processing recover after profound cochlear denervation due to a progressive, compensatory plasticity at higher stages of the central auditory pathway. Lesioning >95% of cochlear nerve afferent synapses, while sparing hair cells, in adult mice virtually eliminated the auditory brainstem response and acoustic startle reflex, yet tone detection behavior was nearly normal. As sound-evoked responses from the auditory nerve grew progressively weaker following denervation, sound-evoked activity in the cortex-and, to a lesser extent, the midbrain-rebounded or surpassed control levels. Increased central gain supported the recovery of rudimentary sound features encoded by firing rate, but not features encoded by precise spike timing such as modulated noise or speech. These findings underscore the importance of central plasticity in the perceptual sequelae of cochlear hearing impairment.

Brain responses in humans reveal ideal observer-like sensitivity to complex acoustic patterns

We use behavioral methods, magnetoencephalography, and functional MRI to investigate how human listeners discover temporal patterns and statistical regularities in complex sound sequences. Sensitivity to patterns is fundamental to sensory processing, in particular in the auditory system, because most auditory signals only have meaning as successions over time. Previous evidence suggests that the brain is tuned to the statistics of sensory stimulation. However, the process through which this arises has been elusive. We demonstrate that listeners are remarkably sensitive to the emergence of complex patterns within rapidly evolving sound sequences, performing on par with an ideal observer model. Brain responses reveal online processes of evidence accumulation--dynamic changes in tonic activity precisely correlate with the expected precision or predictability of ongoing auditory input--both in terms of deterministic (first-order) structure and the entropy of random sequences. Source analysis demonstrates an interaction between primary auditory cortex, hippocampus, and inferior frontal gyrus in the process of discovering the regularity within the ongoing sound sequence. The results are consistent with precision based predictive coding accounts of perceptual inference and provide compelling neurophysiological evidence of the brain's capacity to encode high-order temporal structure in sensory signals.

The Essential Complexity of Auditory Receptive Fields

Encoding properties of sensory neurons are commonly modeled using linear finite impulse response (FIR) filters. For the auditory system, the FIR filter is instantiated in the spectro-temporal receptive field (STRF), often in the framework of the generalized linear model. Despite widespread use of the FIR STRF, numerous formulations for linear filters are possible that require many fewer parameters, potentially permitting more efficient and accurate model estimates. To explore these alternative STRF architectures, we recorded single-unit neural activity from auditory cortex of awake ferrets during presentation of natural sound stimuli. We compared performance of > 1000 linear STRF architectures, evaluating their ability to predict neural responses to a novel natural stimulus. Many were able to outperform the FIR filter. Two basic constraints on the architecture lead to the improved performance: (1) factorization of the STRF matrix into a small number of spectral and temporal filters and (2) low-dimensional parameterization of the factorized filters. The best parameterized model was able to outperform the full FIR filter in both primary and secondary auditory cortex, despite requiring fewer than 30 parameters, about 10% of the number required by the FIR filter. After accounting for noise from finite data sampling, these STRFs were able to explain an average of 40% of A1 response variance. The simpler models permitted more straightforward interpretation of sensory tuning properties. They also showed greater benefit from incorporating nonlinear terms, such as short term plasticity, that provide theoretical advances over the linear model. Architectures that minimize parameter count while maintaining maximum predictive power provide insight into the essential degrees of freedom governing auditory cortical function. They also maximize statistical power available for characterizing additional nonlinear properties that limit current auditory models.

Understanding how the brain solves sensory problems can provide useful insight for the development of automated systems such as speech recognizers and image classifiers. Recent developments in nonlinear regression and machine learning have produced powerful algorithms for characterizing the input-output relationship of complex systems. However, the complexity of sensory neural systems, combined with practical limitations on experimental data, make it difficult to apply arbitrarily complex analyses to neural data. In this study we pushed analysis in the opposite direction, toward simpler models. We asked how simple a model can be while still capturing the essential sensory properties of neurons in auditory cortex. We found that substantially simpler formulations of the widely-used spectro-temporal receptive field are able to perform as well as the best current models. These simpler formulations define new basis sets that can be incorporated into state-of-the-art machine learning algorithms for a more exhaustive exploration of sensory processing.

Synaptic plasticity as a cortical coding scheme

Processing of auditory information requires constant adjustment due to alterations of the environment and changing conditions in the nervous system with age, health, and experience. Consequently, patterns of activity in cortical networks have complex dynamics over a wide range of timescales, from milliseconds to days and longer. In the primary auditory cortex (AI), multiple forms of adaptation and plasticity shape synaptic input and action potential output. However, the variance of neuronal responses has made it difficult to characterize AI receptive fields and to determine the function of AI in processing auditory information such as vocalizations. Here we describe recent studies on the temporal modulation of cortical responses and consider the relation of synaptic plasticity to neural coding.

Frequency-specific adaptation and its underlying circuit model in the auditory midbrain

Receptive fields of sensory neurons are considered to be dynamic and depend on the stimulus history. In the auditory system, evidence of dynamic frequency-receptive fields has been found following stimulus-specific adaptation (SSA). However, the underlying mechanism and circuitry of SSA have not been fully elucidated. Here, we studied how frequency-receptive fields of neurons in rat inferior colliculus (IC) changed when exposed to a biased tone sequence. Pure tone with one specific frequency (adaptor) was presented markedly more often than others. The adapted tuning was compared with the original tuning measured with an unbiased sequence. We found inhomogeneous changes in frequency tuning in IC, exhibiting a center-surround pattern with respect to the neuron's best frequency. Central adaptors elicited strong suppressive and repulsive changes while flank adaptors induced facilitative and attractive changes. Moreover, we proposed a two-layer model of the underlying network, which not only reproduced the adaptive changes in the receptive fields but also predicted novelty responses to oddball sequences. These results suggest that frequency-specific adaptation in auditory midbrain can be accounted for by an adapted frequency channel and its lateral spreading of adaptation, which shed light on the organization of the underlying circuitry.

Sensitivity of rat inferior colliculus neurons to frequency distributions

Stimulus-specific adaptation refers to a neural response reduction to a repeated stimulus that does not generalize to other stimuli. However, stimulus-specific adaptation appears to be influenced by additional factors. For example, the statistical distribution of tone frequencies has recently been shown to dynamically alter stimulus-specific adaptation in human auditory cortex. The present study investigated whether statistical stimulus distributions also affect stimulus-specific adaptation at an earlier stage of the auditory hierarchy. Neural spiking activity and local field potentials were recorded from inferior colliculus neurons of rats while tones were presented in oddball sequences that formed two different statistical contexts. Each sequence consisted of a repeatedly presented tone (standard) and three rare deviants of different magnitudes (small, moderate, large spectral change). The critical manipulation was the relative probability with which large spectral changes occurred. In one context the probability was high (relative to all deviants), while it was low in the other context. We observed larger responses for deviants compared with standards, confirming previous reports of increased response adaptation for frequently presented tones. Importantly, the statistical context in which tones were presented strongly modulated stimulus-specific adaptation. Physically and probabilistically identical stimuli (moderate deviants) in the two statistical contexts elicited different response magnitudes consistent with neural gain changes and thus neural sensitivity adjustments induced by the spectral range of a stimulus distribution. The data show that already at the level of the inferior colliculus stimulus-specific adaptation is dynamically altered by the statistical context in which stimuli occur.

Dynamic encoding of speech sequence probability in human temporal cortex

Sensory processing involves identification of stimulus features, but also integration with the surrounding sensory and cognitive context. Previous work in animals and humans has shown fine-scale sensitivity to context in the form of learned knowledge about the statistics of the sensory environment, including relative probabilities of discrete units in a stream of sequential auditory input. These statistics are a defining characteristic of one of the most important sequential signals humans encounter: speech. For speech, extensive exposure to a language tunes listeners to the statistics of sound sequences. To address how speech sequence statistics are neurally encoded, we used high-resolution direct cortical recordings from human lateral superior temporal cortex as subjects listened to words and nonwords with varying transition probabilities between sound segments. In addition to their sensitivity to acoustic features (including contextual features, such as coarticulation), we found that neural responses dynamically encoded the language-level probability of both preceding and upcoming speech sounds. Transition probability first negatively modulated neural responses, followed by positive modulation of neural responses, consistent with coordinated predictive and retrospective recognition processes, respectively. Furthermore, transition probability encoding was different for real English words compared with nonwords, providing evidence for online interactions with high-order linguistic knowledge. These results demonstrate that sensory processing of deeply learned stimuli involves integrating physical stimulus features with their contextual sequential structure. Despite not being consciously aware of phoneme sequence statistics, listeners use this information to process spoken input and to link low-level acoustic representations with linguistic information about word identity and meaning.

Neuronal oscillations as a mechanistic substrate of auditory temporal prediction

Neuronal oscillations are comprised of rhythmic fluctuations of excitability that are synchronized in ensembles of neurons and thus function as temporal filters that dynamically organize sensory processing. When perception relies on anticipatory mechanisms, ongoing oscillations also provide a neurophysiological substrate for temporal prediction. In this article, we review evidence for this account with a focus on auditory perception. We argue that such "oscillatory temporal predictions" can selectively amplify neuronal sensitivity to inputs that occur in a predicted, task-relevant rhythm and optimize temporal selection. We elaborate this argument for a prototypic example, speech processing, where information is present at multiple time scales, with delta, theta, and low-gamma oscillations being specifically and simultaneously engaged, enabling multiplexing. We then consider the origin of temporal predictions, specifically the idea that the motor system is involved in the generation of such prior information. Finally, we place temporal predictions in the general context of internal models, discussing how they interact with feature-based or spatial predictions. We propose that complementary predictions interact synergistically according to a dominance hierarchy, shaping perception in the form of a multidimensional filter mechanism.

Spatial auditory regularity encoding and prediction: Human middle-latency and long-latency auditory evoked potentials

By encoding acoustic regularities present in the environment, the human brain can generate predictions of what is likely to occur next. Recent studies suggest that deviations from encoded regularities are detected within 10-50ms after stimulus onset, as indicated by electrophysiological effects in the middle latency response (MLR) range. This is upstream of previously known long-latency (LLR) signatures of deviance detection such as the mismatch negativity (MMN) component. In the present study, we created predictable and unpredictable contexts to investigate MLR and LLR signatures of the encoding of spatial auditory regularities and the generation of predictions from these regularities. Chirps were monaurally delivered in an either regular (predictable: left-right-left-right) or a random (unpredictable left/right alternation or repetition) manner. Occasional stimulus omissions occurred in both types of sequences. Results showed that the Na component (peaking at 34ms after stimulus onset) was attenuated for regular relative to random chirps, albeit no differences were observed for stimulus omission responses in the same latency range. In the LLR range, larger chirp-and omission-evoked responses were elicited for the regular than for the random condition, and predictability effects were more prominent over the right hemisphere. We discuss our findings in the framework of a hierarchical organization of spatial regularity encoding. This article is part of a Special Issue entitled SI: Prediction and Attention.

Predictive motor control of sensory dynamics in auditory active sensing

Neuronal oscillations present potential physiological substrates for brain operations that require temporal prediction. We review this idea in the context of auditory perception. Using speech as an exemplar, we illustrate how hierarchically organized oscillations can be used to parse and encode complex input streams. We then consider the motor system as a major source of rhythms (temporal priors) in auditory processing, that act in concert with attention to sharpen sensory representations and link them across areas. We discuss the circuits that could mediate this audio-motor interaction, notably the potential role of the somatosensory system. Finally, we reposition temporal predictions in the context of internal models, discussing how they interact with feature-based or spatial predictions. We argue that complementary predictions interact synergistically according to the organizational principles of each sensory system, forming multidimensional filters crucial to perception.

The effect of adaptation on the tuning curves of rat auditory cortex

Repeated stimulus causes a specific suppression of neuronal responses, which is so-called as Stimulus-Specific Adaptation (SSA). This effect can be recovered when the stimulus changes. In the auditory system SSA is a well-known phenomenon that appears at different levels of the mammalian auditory pathway. In this study, we explored the effects of adaptation to a particular stimulus on the auditory tuning curves of anesthetized rats. We used two sequences and compared the responses of each tone combination in these two conditions. First sequence consists of different pure tone combinations that were presented randomly. In the second one, the same stimuli of the first sequence were presented in the context of an adapted stimulus (adapter) that occupied 80% of sequence probability. The population results demonstrated that the adaptation factor decreased the frequency response area and made a change in the tuning curve to shift it unevenly toward the higher thresholds of tones. The local field potentials and multi-unit activity responses have indicated that the neural activities strength of the adapted frequency has been suppressed as well as with lower suppression in neighboring frequencies. This aforementioned reduction changed the characteristic frequency of the tuning curve.

Rapid and Direct Encoding of Numerosity in the Visual Stream

Humans are endowed with an intuitive number sense that allows us to perceive and estimate numerosity without relying on language. It is controversial, however, as to whether there is a neural mechanism for direct perception of numerosity or whether numerosity is perceived indirectly via other perceptual properties. In this study, we used a novel regression-based analytic method, which allowed an assessment of the unique contributions of visual properties, including numerosity, to explain visual evoked potentials of participants passively viewing dot arrays. We found that the human brain is uniquely sensitive to numerosity and more sensitive to changes in numerosity than to changes in other visual properties, starting extremely early in the visual stream: 75 ms over a medial occipital site and 180 ms over bilateral occipitoparietal sites. These findings provide strong evidence for the existence of a neural mechanism for rapidly and directly extracting numerosity information in the human visual pathway.

Auditory cortical processing in real-world listening: the auditory system going real

The auditory sense of humans transforms intrinsically senseless pressure waveforms into spectacularly rich perceptual phenomena: the music of Bach or the Beatles, the poetry of Li Bai or Omar Khayyam, or more prosaically the sense of the world filled with objects emitting sounds that is so important for those of us lucky enough to have hearing. Whereas the early representations of sounds in the auditory system are based on their physical structure, higher auditory centers are thought to represent sounds in terms of their perceptual attributes. In this symposium, we will illustrate the current research into this process, using four case studies. We will illustrate how the spectral and temporal properties of sounds are used to bind together, segregate, categorize, and interpret sound patterns on their way to acquire meaning, with important lessons to other sensory systems as well.

Statistical context shapes stimulus-specific adaptation in human auditory cortex

Stimulus-specific adaptation is the phenomenon whereby neural response magnitude decreases with repeated stimulation. Inconsistencies between recent nonhuman animal recordings and computational modeling suggest dynamic influences on stimulus-specific adaptation. The present human electroencephalography (EEG) study investigates the potential role of statistical context in dynamically modulating stimulus-specific adaptation by examining the auditory cortex-generated N1 and P2 components. As in previous studies of stimulus-specific adaptation, listeners were presented with oddball sequences in which the presentation of a repeated tone was infrequently interrupted by rare spectral changes taking on three different magnitudes. Critically, the statistical context varied with respect to the probability of small versus large spectral changes within oddball sequences (half of the time a small change was most probable; in the other half a large change was most probable). We observed larger N1 and P2 amplitudes (i.e., release from adaptation) for all spectral changes in the small-change compared with the large-change statistical context. The increase in response magnitude also held for responses to tones presented with high probability, indicating that statistical adaptation can overrule stimulus probability per se in its influence on neural responses. Computational modeling showed that the degree of coadaptation in auditory cortex changed depending on the statistical context, which in turn affected stimulus-specific adaptation. Thus the present data demonstrate that stimulus-specific adaptation in human auditory cortex critically depends on statistical context. Finally, the present results challenge the implicit assumption of stationarity of neural response magnitudes that governs the practice of isolating established deviant-detection responses such as the mismatch negativity.

Contradictory behavioral biases result from the influence of past stimuli on perception

Biases such as the preference of a particular response for no obvious reason, are an integral part of psychophysics. Such biases have been reported in the common two-alternative forced choice (2AFC) experiments, where participants are instructed to compare two consecutively presented stimuli. However, the principles underlying these biases are largely unknown and previous studies have typically used ad-hoc explanations to account for them. Here we consider human performance in the 2AFC tone frequency discrimination task, utilizing two standard protocols. In both protocols, each trial contains a reference stimulus. In one (Reference-Lower protocol), the frequency of the reference stimulus is always lower than that of the comparison stimulus, whereas in the other (Reference protocol), the frequency of the reference stimulus is either lower or higher than that of the comparison stimulus. We find substantial interval biases. Namely, participants perform better when the reference is in a specific interval. Surprisingly, the biases in the two experiments are opposite: performance is better when the reference is in the first interval in the Reference protocol, but is better when the reference is second in the Reference-Lower protocol. This inconsistency refutes previous accounts of the interval bias, and is resolved when experiments statistics is considered. Viewing perception as incorporation of sensory input with prior knowledge accumulated during the experiment accounts for the seemingly contradictory biases both qualitatively and quantitatively. The success of this account implies that even simple discriminations reflect a combination of sensory limitations, memory limitations, and the ability to utilize stimuli statistics.

Moving sensory adaptation beyond suppressive effects in single neurons

How an object is perceived depends on the temporal context in which it is encountered. Sensory signals in the brain also depend on temporal context, a phenomenon often referred to as adaptation. Traditional descriptions of adaptation effects emphasize various forms of response fatigue in single neurons, which grow in strength with exposure to a stimulus. Recent work on vision, and other sensory modalities, has shown that this description has substantial shortcomings. Here we review our emerging understanding of how adaptation alters the balance between excitatory and suppressive signals, how effects depend on adaptation duration, and how adaptation influences representations that are distributed within and across multiple brain structures. This work points to a sophisticated set of mechanisms for adjusting to recent sensory experience, and suggests new avenues for understanding their function.

Stimulus-specific adaptation and deviance detection in the auditory system: experiments and models

Stimulus-specific adaptation (SSA) is the reduction in the response to a common stimulus that does not generalize, or only partially generalizes, to other, rare stimuli. SSA has been proposed to be a correlate of 'deviance detection', an important computational task of sensory systems. SSA is ubiquitous in the auditory system: It is found both in cortex and in subcortical stations, and it has been demonstrated in many mammalian species as well as in birds. A number of models have been suggested in the literature to account for SSA in the auditory domain. In this review, the experimental literature is critically examined in relationship to these models. While current models can all account for auditory SSA to some degree, none is fully compatible with the available findings.

Auditory evoked fields measured noninvasively with small-animal MEG reveal rapid repetition suppression in the guinea pig

In animal models, single-neuron response properties such as stimulus-specific adaptation have been described as possible precursors to mismatch negativity, a human brain response to stimulus change. In the present study, we attempted to bridge the gap between human and animal studies by characterising responses to changes in the frequency of repeated tone series in the anesthetised guinea pig using small-animal magnetoencephalography (MEG). We showed that 1) auditory evoked fields (AEFs) qualitatively similar to those observed in human MEG studies can be detected noninvasively in rodents using small-animal MEG; 2) guinea pig AEF amplitudes reduce rapidly with tone repetition, and this AEF reduction is largely complete by the second tone in a repeated series; and 3) differences between responses to the first (deviant) and later (standard) tones after a frequency transition resemble those previously observed in awake humans using a similar stimulus paradigm.

How does the extraction of local and global auditory regularities vary with context?

How does the human brain extract regularities from its environment? There is evidence that short range or ‘local’ regularities (within seconds) are automatically detected by the brain while long range or ‘global’ regularities (over tens of seconds or more) require conscious awareness. In the present experiment, we asked whether participants' attention was needed to acquire such auditory regularities, to detect their violation or both. We designed a paradigm in which participants listened to predictable sounds. Subjects could be distracted by a visual task at two moments: when they were first exposed to a regularity or when they detected violations of this regularity. MEG recordings revealed that early brain responses (100–130 ms) to violations of short range regularities were unaffected by visual distraction and driven essentially by local transitional probabilities. Based on global workspace theory and prior results, we expected that visual distraction would eliminate the long range global effect, but unexpectedly, we found the contrary, i.e. late brain responses (300–600 ms) to violations of long range regularities on audio-visual trials but not on auditory only trials. Further analyses showed that, in fact, visual distraction was incomplete and that auditory and visual stimuli interfered in both directions. Our results show that conscious, attentive subjects can learn the long range dependencies present in auditory stimuli even while performing a visual task on synchronous visual stimuli. Furthermore, they acquire a complex regularity and end up making different predictions for the very same stimulus depending on the context (i.e. absence or presence of visual stimuli). These results suggest that while short-range regularity detection is driven by local transitional probabilities between stimuli, the human brain detects and stores long-range regularities in a highly flexible, context dependent manner.

Online stimulus optimization rapidly reveals multidimensional selectivity in auditory cortical neurons

Neurons in sensory brain regions shape our perception of the surrounding environment through two parallel operations: decomposition and integration. For example, auditory neurons decompose sounds by separately encoding their frequency, temporal modulation, intensity, and spatial location. Neurons also integrate across these various features to support a unified perceptual gestalt of an auditory object. At higher levels of a sensory pathway, neurons may select for a restricted region of feature space defined by the intersection of multiple, independent stimulus dimensions. To further characterize how auditory cortical neurons decompose and integrate multiple facets of an isolated sound, we developed an automated procedure that manipulated five fundamental acoustic properties in real time based on single-unit feedback in awake mice. Within several minutes, the online approach converged on regions of the multidimensional stimulus manifold that reliably drove neurons at significantly higher rates than predefined stimuli. Optimized stimuli were cross-validated against pure tone receptive fields and spectrotemporal receptive field estimates in the inferior colliculus and primary auditory cortex. We observed, from midbrain to cortex, increases in both level invariance and frequency selectivity, which may underlie equivalent sparseness of responses in the two areas. We found that onset and steady-state spike rates increased proportionately as the stimulus was tailored to the multidimensional receptive field. By separately evaluating the amount of leverage each sound feature exerted on the overall firing rate, these findings reveal interdependencies between stimulus features as well as hierarchical shifts in selectivity and invariance that may go unnoticed with traditional approaches.

Adaptation of high-gamma responses in human auditory association cortex

This study investigates adaptation of high-frequency cortical responses [>60 Hz; high-gamma (HG)] to simple and complex sounds in human nonprimary auditory cortex. We used intracranial electrocorticographic recordings to measure event-related changes in HG power as a function of stimulus probability. Tone and speech stimuli were presented in a series of traditional oddball and control paradigms. We hypothesized that HG power attenuates with stimulus repetition over multiple concurrent time scales in auditory association cortex. Time-frequency analyses were performed to identify auditory-responsive sites. Single-trial analyses and quantitative modeling were then used to measure trial-to-trial changes in HG power for high (frequent), low (infrequent), and equal (control) stimulus probabilities. Results show strong reduction of HG responses to frequently repeated tones and speech, with no differences in responses to infrequent and equal-probability stimuli. Adaptation of the HG frequent response, and not stimulus-acoustic differences or deviance-detection enhancement effects, accounted for the differential responses observed for frequent and infrequent sounds. Adaptation of HG responses showed a rapid onset (less than two trials) with slower adaptation between consecutive, repeated trials (2-10 s) and across trials in a stimulus block (∼7 min). The auditory-evoked N100 response also showed repetition-related adaptation, consistent with previous human scalp and animal single-unit recordings. These findings indicate that HG responses are highly sensitive to the regularities of simple and complex auditory events and show adaptation on multiple concurrent time scales in human auditory association cortex.

Encoding of nested levels of acoustic regularity in hierarchically organized areas of the human auditory cortex

Our auditory system is able to encode acoustic regularity of growing levels of complexity to model and predict incoming events. Recent evidence suggests that early indices of deviance detection in the time range of the middle-latency responses (MLR) precede the mismatch negativity (MMN), a well-established error response associated with deviance detection. While studies suggest that only the MMN, but not early deviance-related MLR, underlie complex regularity levels, it is not clear whether these two mechanisms interplay during scene analysis by encoding nested levels of acoustic regularity, and whether neuronal sources underlying local and global deviations are hierarchically organized. We registered magnetoencephalographic evoked fields to rapidly presented four-tone local sequences containing a frequency change. Temporally integrated local events, in turn, defined global regularities, which were infrequently violated by a tone repetition. A global magnetic mismatch negativity (MMNm) was obtained at 140-220 ms when breaking the global regularity, but no deviance-related effects were shown in early latencies. Conversely, Nbm (45-55 ms) and Pbm (60-75 ms) deflections of the MLR, and an earlier MMNm response at 120-160 ms, responded to local violations. Distinct neuronal generators in the auditory cortex underlay the processing of local and global regularity violations, suggesting that nested levels of complexity of auditory object representations are represented in separated cortical areas. Our results suggest that the different processing stages and anatomical areas involved in the encoding of auditory representations, and the subsequent detection of its violations, are hierarchically organized in the human auditory cortex.

Prior Expectations Evoke Stimulus Templates in the Primary Visual Cortex

Sensory processing is strongly influenced by prior expectations. Valid expectations have been shown to lead to improvements in perception as well as in the quality of sensory representations in primary visual cortex. However, very little is known about the neural correlates of the expectations themselves. Previous studies have demonstrated increased activity in sensory cortex following the omission of an expected stimulus, yet it is unclear whether this increased activity constitutes a general surprise signal or rather has representational content. One intriguing possibility is that top–down expectation leads to the formation of a template of the expected stimulus in visual cortex, which can then be compared with subsequent bottom–up input. To test this hypothesis, we used fMRI to noninvasively measure neural activity patterns in early visual cortex of human participants during expected but omitted visual stimuli. Our results show that prior expectation of a specific visual stimulus evokes a feature-specific pattern of activity in the primary visual cortex (V1) similar to that evoked by the corresponding actual stimulus. These results are in line with the notion that prior expectation triggers the formation of specific stimulus templates to efficiently process expected sensory inputs.

Dynamic speech representations in the human temporal lobe

Speech perception requires rapid integration of acoustic input with context-dependent knowledge. Recent methodological advances have allowed researchers to identify underlying information representations in primary and secondary auditory cortex and to examine how context modulates these representations. We review recent studies that focus on contextual modulations of neural activity in the superior temporal gyrus (STG), a major hub for spectrotemporal encoding. Recent findings suggest a highly interactive flow of information processing through the auditory ventral stream, including influences of higher-level linguistic and metalinguistic knowledge, even within individual areas. Such mechanisms may give rise to more abstract representations, such as those for words. We discuss the importance of characterizing representations of context-dependent and dynamic patterns of neural activity in the approach to speech perception research.

Intracellular correlates of stimulus-specific adaptation

Stimulus-specific adaptation (SSA) is the reduction in response to a common stimulus that does not generalize, or only partially generalizes, to rare stimuli. SSA is strong and widespread in primary auditory cortex (A1) of rats, but is weak or absent in the main input station to A1, the ventral division of the medial geniculate body. To study SSA in A1, we recorded neural activity in A1 intracellularly using sharp electrodes. We studied the responses to tone pips of the same frequency in different contexts: as Standard and Deviants in Oddball sequences; in equiprobable sequences; in sequences consisting of rare tone presentations; and in sequences composed of many different frequencies, each of which was rare. SSA was found both in subthreshold membrane potential fluctuations and in spiking responses of A1 neurons. SSA for changes in frequency was large at a frequency difference of 44% between Standard and Deviant, and clearly present with tones separated by as little as 4%, near the behavioral frequency difference limen in rats. When using equivalent measures, SSA in spiking responses was generally larger than the SSA at the level of the membrane potential. This effect can be traced to the nonlinearity of the transformation between membrane potential to spikes. Using the responses to the same tone in different contexts made it possible to demonstrate that cortical SSA could not be fully explained by adaptation in narrow frequency channels, even at the level of the membrane potential. We conclude that local processing significantly contributes to the generation of cortical SSA.