scholarly journals Theta and Gamma Bands Encode Acoustic Dynamics over Wide-ranging Timescales

2019 ◽  
Author(s):  
Xiangbin Teng ◽  
David Poeppel
Keyword(s):  
Auditory Cortex ◽  
Auditory System ◽  
Temporal Coding ◽  
Sound Recognition ◽  
Auditory Information ◽  
Natural Sounds ◽  
Acoustic Information ◽  
Human Auditory System ◽  
Temporal Structures ◽  
Human Auditory Cortex

AbstractNatural sounds have broadband modulation spectra and contain acoustic dynamics ranging from tens to hundreds of milliseconds. How does the human auditory system encode acoustic information over wide-ranging timescales to achieve sound recognition? Previous work (Teng et al., 2017) demonstrated a temporal coding preference in the auditory system for the theta (4 – 7 Hz) and gamma (30 – 45 Hz) ranges, but it remains unclear how acoustic dynamics between these two ranges is encoded. Here we generated artificial sounds with temporal structures over timescales from ~200 ms to ~30 ms and investigated temporal coding on different timescales in the human auditory cortex. Participants discriminated sounds with temporal structures at different timescales while undergoing magnetoencephalography (MEG) recording. The data show robust neural entrainment in the theta and the gamma bands, but not in the alpha and beta bands. Classification analyses as well as stimulus reconstruction reveal that the acoustic information of all timescales can be differentiated through the theta and gamma bands, but the acoustic dynamics in the theta and gamma ranges are preferentially encoded. We replicate earlier findings of multi-time scale processing and further demonstrate that the theta and gamma bands show generality of temporal coding across all timescales with comparable capacity. The results support the hypothesis that the human auditory cortex primarily encodes auditory information employing neural processes within two discrete temporal regimes.SignificanceNatural sounds contain rich acoustic dynamics over wide-ranging timescales, but perceptually relevant regularities often occupy specific temporal ranges. For instance, speech carries phonemic information on a shorter timescale than syllabic information at ~ 200 ms. How does the brain efficiently ‘sample’ continuous acoustic input to perceive temporally structured sounds? We presented sounds with temporal structures at different timescales and measured cortical entrainment using magnetoencephalography. We found, unexpectedly, that the human auditory system preserves high temporal coding precision on two non-overlapping timescales, the slower (theta) and faster (gamma) bands, to track acoustic dynamics over all timescales. The results suggest that the acoustic environment which we experience as seamless and continuous is segregated by discontinuous neural processing, or ‘sampled.’

scholarly journals Theta and Gamma Bands Encode Acoustic Dynamics over Wide-Ranging Timescales

2019 ◽  
Vol 30 (4) ◽  
pp. 2600-2614 ◽  
Author(s):  
Xiangbin Teng ◽  
David Poeppel
Keyword(s):  
Auditory System ◽  
Phase Coherence ◽  
Temporal Coding ◽  
Sound Recognition ◽  
Natural Sounds ◽  
Perceptual Analysis ◽  
Acoustic Information ◽  
Human Auditory System ◽  
Temporal Structures ◽  
Stimulus Reconstruction

Abstract Natural sounds contain acoustic dynamics ranging from tens to hundreds of milliseconds. How does the human auditory system encode acoustic information over wide-ranging timescales to achieve sound recognition? Previous work (Teng et al. 2017) demonstrated a temporal coding preference for the theta and gamma ranges, but it remains unclear how acoustic dynamics between these two ranges are coded. Here, we generated artificial sounds with temporal structures over timescales from ~200 to ~30 ms and investigated temporal coding on different timescales. Participants discriminated sounds with temporal structures at different timescales while undergoing magnetoencephalography recording. Although considerable intertrial phase coherence can be induced by acoustic dynamics of all the timescales, classification analyses reveal that the acoustic information of all timescales is preferentially differentiated through the theta and gamma bands, but not through the alpha and beta bands; stimulus reconstruction shows that the acoustic dynamics in the theta and gamma ranges are preferentially coded. We demonstrate that the theta and gamma bands show the generality of temporal coding with comparable capacity. Our findings provide a novel perspective—acoustic information of all timescales is discretised into two discrete temporal chunks for further perceptual analysis.

Temporal Coherence Sensitivity in Auditory Cortex

2002 ◽  
Vol 88 (5) ◽  
pp. 2684-2699 ◽  
Author(s):  
Dennis L. Barbour ◽  
Xiaoqin Wang
Keyword(s):  
Auditory Cortex ◽  
Auditory System ◽  
Carrier Frequency ◽  
Cortical Neurons ◽  
Neuronal Response ◽  
Primary Auditory Cortex ◽  
Temporal Coherence ◽  
Natural Sounds ◽  
Frequency Changes ◽  
Modulation Phase

Natural sounds often contain energy over a broad spectral range and consequently overlap in frequency when they occur simultaneously; however, such sounds under normal circumstances can be distinguished perceptually (e.g., the cocktail party effect). Sound components arising from different sources have distinct (i.e., incoherent) modulations, and incoherence appears to be one important cue used by the auditory system to segregate sounds into separately perceived acoustic objects. Here we show that, in the primary auditory cortex of awake marmoset monkeys, many neurons responsive to amplitude- or frequency-modulated tones at a particular carrier frequency [the characteristic frequency (CF)] also demonstrate sensitivity to the relative modulation phase between two otherwise identically modulated tones: one at CF and one at a different carrier frequency. Changes in relative modulation phase reflect alterations in temporal coherence between the two tones, and the most common neuronal response was found to be a maximum of suppression for the coherent condition. Coherence sensitivity was generally found in a narrow frequency range in the inhibitory portions of the frequency response areas (FRA), indicating that only some off-CF neuronal inputs into these cortical neurons interact with on-CF inputs on the same time scales. Over the population of neurons studied, carrier frequencies showing coherence sensitivity were found to coincide with the carrier frequencies of inhibition, implying that inhibitory inputs create the effect. The lack of strong coherence-induced facilitation also supports this interpretation. Coherence sensitivity was found to be greatest for modulation frequencies of 16–128 Hz, which is higher than the phase-locking capability of most cortical neurons, implying that subcortical neurons could play a role in the phenomenon. Collectively, these results reveal that auditory cortical neurons receive some off-CF inputs temporally matched and some temporally unmatched to the on-CF input(s) and respond in a fashion that could be utilized by the auditory system to segregate natural sounds containing similar spectral components (such as vocalizations from multiple conspecifics) based on stimulus coherence.

Neural Population Coding of Natural Sounds in Non-flying Mammals

2017 ◽  
Author(s):  
Israel Nelken
Keyword(s):  
Auditory Cortex ◽  
Auditory System ◽  
Cortical Neurons ◽  
Dominant Role ◽  
Primary Auditory Cortex ◽  
Population Coding ◽  
Neural Population ◽  
Good Effect ◽  
Natural Sounds ◽  
Complex Sounds

Understanding the principles by which sensory systems represent natural stimuli is one of the holy grails of neuroscience. In the auditory system, the study of the coding of natural sounds has a particular prominence. Indeed, the relationships between neural responses to simple stimuli (usually pure tone bursts)—often used to characterize auditory neurons—and complex sounds (in particular natural sounds) may be complex. Many different classes of natural sounds have been used to study the auditory system. Sound families that researchers have used to good effect in this endeavor include human speech, species-specific vocalizations, an “acoustic biotope” selected in one way or another, and sets of artificial sounds that mimic important features of natural sounds. Peripheral and brainstem representations of natural sounds are relatively well understood. The properties of the peripheral auditory system play a dominant role, and further processing occurs mostly within the frequency channels determined by these properties. At the level of the inferior colliculus, the highest brainstem station, representational complexity increases substantially due to the convergence of multiple processing streams. Undoubtedly, the most explored part of the auditory system, in term of responses to natural sounds, is the primary auditory cortex. In spite of over 50 years of research, there is still no commonly accepted view of the nature of the population code for natural sounds in the auditory cortex. Neurons in the auditory cortex are believed by some to be primarily linear spectro-temporal filters, by others to respond to conjunctions of important sound features, or even to encode perceptual concepts such as “auditory objects.” Whatever the exact mechanism is, many studies consistently report a substantial increase in the variability of the response patterns of cortical neurons to natural sounds. The generation of such variation may be the main contribution of auditory cortex to the coding of natural sounds.

scholarly journals Temporal Dynamics of Adaptation to Natural Sounds in the Human Auditory Cortex

2007 ◽  
Vol 18 (6) ◽  
pp. 1350-1360 ◽  
Author(s):  
C. F. Altmann ◽  
H. Nakata ◽  
Y. Noguchi ◽  
K. Inui ◽  
M. Hoshiyama ◽  
...  
Keyword(s):  
Auditory Cortex ◽  
Temporal Dynamics ◽  
Natural Sounds ◽  
Human Auditory Cortex

scholarly journals Processing of Natural Sounds: Characterization of Multipeak Spectral Tuning in Human Auditory Cortex

2013 ◽  
Vol 33 (29) ◽  
pp. 11888-11898 ◽  
Author(s):  
M. Moerel ◽  
F. De Martino ◽  
R. Santoro ◽  
K. Ugurbil ◽  
R. Goebel ◽  
...  
Keyword(s):  
Auditory Cortex ◽  
Spectral Tuning ◽  
Natural Sounds ◽  
Human Auditory Cortex

scholarly journals Adaptive efficient coding of correlated acoustic properties

2019 ◽  
Author(s):  
Kai Lu ◽  
Wanyi Liu ◽  
Kelsey Dutta ◽  
Jonathan B. Fritz ◽  
Shihab A. Shamma
Keyword(s):  
Auditory Cortex ◽  
Auditory System ◽  
Neural Coding ◽  
Sensory Systems ◽  
Temporal Coding ◽  
Acoustic Properties ◽  
Efficient Coding ◽  
Single Dimension ◽  
Passive Exposure ◽  
Orthogonal Dimension

AbstractNatural sounds such as vocalizations often have co-varying acoustic attributes where one acoustic feature can be predicted from another, resulting in redundancy in neural coding. It has been proposed that sensory systems are able to detect such covariation and adapt to reduce redundancy, leading to more efficient neural coding. Results of recent psychoacoustic studies suggest that, following passive exposure to sounds in which temporal and spectral attributes covaried in a correlated fashion, the auditory system adapts to efficiently encode the two co-varying dimensions as a single dimension, at the cost of lost sensitivity to the orthogonal dimension. Here we explore the neural basis of this psychophysical phenomenon by recording single-unit responses from primary auditory cortex (A1) in awake ferrets exposed passively to stimuli with two correlated attributes in the temporal and spectral domain similar to that utilized in the psychoacoustic experiments. We found that: (1) the signal-to-noise (SNR) ratio of spike rate coding of cortical responses driven by sounds with correlated attributes was reduced along the orthogonal dimension; while the SNR ratio remained intact along the exposure dimension; (2) Mutual information of spike temporal coding increased only along the exposure dimension; (3) correlation between neurons tuned to the two covarying attributes decreased after exposure; (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 Learning Hypothesis and may deepen our understanding of how the auditory system represents acoustic regularities and covariance.SignificanceIn the Efficient Coding (EC) hypothesis, proposed by Barlow in 1961, 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 are consistent with the EC hypothesis, showing that following passive exposure to stimuli with correlated attributes, the auditory system adapts so as to more efficiently encode the two co-varying dimensions as a single dimension. In the current neurophysiological experiments, using a similar stimulus design and experimental paradigm to the psychoacoustic studies of Stilp and colleagues (2010, 2011, 2012, 2016), we recorded responses from single neurons in the auditory cortex of the awake ferret, showing adaptive efficient neural coding of correlated acoustic properties.

scholarly journals Encoding of Natural Sounds at Multiple Spectral and Temporal Resolutions in the Human Auditory Cortex

2014 ◽  
Vol 10 (1) ◽  
pp. e1003412 ◽  
Author(s):  
Roberta Santoro ◽  
Michelle Moerel ◽  
Federico De Martino ◽  
Rainer Goebel ◽  
Kamil Ugurbil ◽  
...  
Keyword(s):  
Auditory Cortex ◽  
Natural Sounds ◽  
Human Auditory Cortex

Processing of location and pattern changes of natural sounds in the human auditory cortex

NeuroImage ◽  
2007 ◽  
Vol 35 (3) ◽  
pp. 1192-1200 ◽  
Author(s):  
Christian F. Altmann ◽  
Christoph Bledowski ◽  
Michael Wibral ◽  
Jochen Kaiser
Keyword(s):  
Auditory Cortex ◽  
Natural Sounds ◽  
Human Auditory Cortex

scholarly journals Distinct neural ensemble response statistics are associated with recognition and discrimination of natural sound textures

2020 ◽  
Author(s):  
X. Zhai ◽  
F. Khatami ◽  
M. Sadeghi ◽  
F. He ◽  
H.L. Read ◽  
...  
Keyword(s):  
Auditory System ◽  
High Order ◽  
Sound Recognition ◽  
Summary Statistics ◽  
Computer Algorithms ◽  
Natural Sounds ◽  
Sound Structure ◽  
Neural Ensemble ◽  
Natural Sound ◽  
Sound Spectrum

ABSTRACTThe perception of sound textures, a class of natural sounds defined by statistical sound structure such as fire wind, and rain, has been proposed to arise through the integration of time-averaged summary statistics. Where and how the auditory system might encode these summary statistics to create internal representations of these stationary sounds, however, is unknown. Here, using natural textures and synthetic variants with reduced statistics, we show that summary statistics modulate the correlations between frequency organized neuron ensembles in the awake rabbit inferior colliculus. These neural ensemble correlation statistics capture high-order sound structure and allow for accurate neural decoding in a single trial recognition task with evidence accumulation times approaching 1 s. In contrast, the average activity across the neural ensemble (neural spectrum) provides a fast (tens of ms) and salient signal that contributes primarily to texture discrimination. Intriguingly, perceptual studies in human listeners reveals analogous trends: the sound spectrum is integrated quickly and serves as salient discrimination cue while high-order sound statistics are integrated slowly and contribute substantially more towards recognition. The findings suggest statistical sound cues such as the sound spectrum and correlation structure are represented by distinct response statistics in auditory midbrain ensembles, and that these neural response statistics may have dissociable roles and time scales for the recognition and discrimination of natural sounds.SIGNIFICANCE STATEMENTBeing able to recognize and discriminate natural sounds, such as from a running stream, a crowd clapping, or ruffling leaves is a critical task of the normal functioning auditory system. Humans can easily perform such tasks, yet they can be particularly difficult for the hearing impaired and they challenge our most sophisticated computer algorithms. This difficulty is attributed to the complex physical structure of such natural sounds and the fact they are not unique: they vary randomly in a statistically defined manner from one excerpt to the other. Here we provide the first evidence, to our knowledge, that the central auditory system is able to encode and utilize statistical sound cues for natural sound recognition and discrimination behaviors.

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