A neural ensemble correlation code for sound category identification

Sadeghi, Mina; Xiu, Zhai; Stevenson, I. H.; Escabı́, Monty A.

doi:10.1371/journal.pbio.3000449

Cited by 26 publications

(29 citation statements)

References 57 publications

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“…Where and how the brain represents summary statistics in neural activity and how these contributes to recognition and discrimination for real world sounds is largely unknown. Neural activity throughout the auditory pathway is known to be modulated by a variety of statistical cues such as the sound contrast, modulation power spectrum, and correlation structure [4][5][6][7][8][9][10][11][12] . However, it has been less clear whether and to what degree summary statistics serve as informative stimulus dimensions that directly contribute to sound recognition.…”

Section: Introductionmentioning

confidence: 99%

“…Neurons in the IC are selective over most of the perceptually relevant range of sound modulations and neural activity is strongly driven by multiple high-order sound statistics [4][5][6][7]10 . In previous work, we showed the correlation statistics of natural sounds are highly informative about stimulus identity and they appear to be represented in the correlation statistics of auditory midbrain neuron ensembles 4 . Correlations between neurons have also been proposed as mechanisms for pitch identification 13 and sound localization 14 .…”

Section: Introductionmentioning

confidence: 99%

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

Xiu

Khatami

Sadeghi

et al. 2020

Proc. Natl. Acad. Sci. U.S.A.

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The 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 (IC). 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 milliseconds) and salient signal that contributes primarily to texture discrimination. Intriguingly, perceptual studies in human listeners reveal analogous trends: the sound spectrum is integrated quickly and serves as a salient discrimination cue while high-order sound statistics are integrated slowly and contribute substantially more toward 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.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Xiu

Khatami

Sadeghi

et al. 2020

Proc. Natl. Acad. Sci. U.S.A.

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“…To determine how natural sound statistics influence the response statistics of neural ensembles in IC, we first characterized several key statistics from an auditory model representation for five natural sound recordings. These include sounds from a crackling fire, bird chorus, outdoor crowd, running water, and a rattling snake (Supplementary sounds [1][2][3][4][5]. We then used texture synthesis 1 to generate synthetic sound variants with perturbed low-and high-order statistics (see METHODS).…”

Section: Natural Sound Texture Statistics Modulate Neural Correlationmentioning

confidence: 99%

“…To assess the contribution of nonspecific neural activity, we also estimated noise correlations, which are the result of stimulus-independent network activity. The noise correlations were obtained by subtracting the shuffled (different trials) from the unshuffled (same trials) crosscorrelations 4 . Similar to the shuffled correlation, the unshuffled correlation between recording channels k and l was given as: where c *7 0R:8@ 1, < is also bounded between -1 and 1.…”

Section: Population Response Metrics: Neural Correlations and Neural mentioning

confidence: 99%

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

Xiu

Khatami

Sadeghi

et al. 2020

Preprint

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The 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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Behavioral Motor Performance

Leib,

Howard,

Millard

et al. 2023

Comprehensive Physiology

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The human sensorimotor control system has exceptional abilities to perform skillful actions. We easily switch between strenuous tasks that involve brute force, such as lifting a heavy sewing machine, and delicate movements such as threading a needle in the same machine. Using a structure with different control architectures, the motor system is capable of updating its ability to perform through our daily interaction with the fluctuating environment. However, there are issues that make this a difficult computational problem for the brain to solve. The brain needs to control a nonlinear, nonstationary neuromuscular system, with redundant and occasionally undesired degrees of freedom, in an uncertain environment using a body in which information transmission is subject to delays and noise. To gain insight into the mechanisms of motor control, here we survey movement laws and invariances that shape our everyday motion. We then examine the major solutions to each of these problems in the three parts of the sensorimotor control system, sensing, planning, and acting. We focus on how the sensory system, the control architectures, and the structure and operation of the muscles serve as complementary mechanisms to overcome deviations and disturbances to motor behavior and give rise to skillful motor performance. We conclude with possible future research directions based on suggested links between the operation of the sensorimotor system across the movement stages. © 2024 American Physiological Society. Compr Physiol 14:5179‐5224, 2024.

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A neural ensemble correlation code for sound category identification

Cited by 26 publications

References 57 publications

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

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

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

Behavioral Motor Performance

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