Neural coding strategies in auditory cortex

Wang, Xiaoqin

doi:10.1016/j.heares.2007.01.019

Cited by 117 publications

(97 citation statements)

References 69 publications

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“…Far less attention has been paid to higher-order statistics in audition (10,11). However, there is good evidence that auditory cortical representations decreasingly correspond with physical stimulus dimensions (21,29,30), and this may be similar to the loss of acoustic dimensions (AD, SS) seen here, as more efficient dimensions better capture perceptual performance.…”

Section: Discussionsupporting

confidence: 65%

Rapid efficient coding of correlated complex acoustic properties

Stilp

Rogers

Kluender

2010

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

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Natural sounds are complex, typically changing along multiple acoustic dimensions that covary in accord with physical laws governing sound-producing sources. We report that, after passive exposure to novel complex sounds, highly correlated features initially collapse onto a single perceptual dimension, capturing covariance at the expense of unitary stimulus dimensions. Discriminability of sounds respecting the correlation is maintained, but is temporarily lost for sounds orthogonal or oblique to experienced covariation. Following extended experience, perception of variance not captured by the correlation is restored, but weighted only in proportion to total experienced covariance. A Hebbian neural network model captures some aspects of listener performance; an anti-Hebbian model captures none; but, a principal components analysis model captures the full pattern of results. Predictions from the principal components analysis model also match evolving listener performance in two discrimination tasks absent passive listening. These demonstrations of adaptation to correlated attributes provide direct behavioral evidence for efficient coding.auditory perception | cortical models | perceptual organization

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Section: Discussionsupporting

confidence: 65%

Rapid efficient coding of correlated complex acoustic properties

Stilp

Rogers

Kluender

2010

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

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“…Although overall responses were not significantly more sparse in A1 than ICc, a prominent hypothesis for sound coding at higher levels of the central auditory pathways holds that the sustained nature of stimulus-evoked responses, rather than the overall rate, is indicative of stimulus preference Wang, 2007). Namely, responses in A1 single units in awake an- E, Peak firing rates from A1 (blue) and ICc (red) were calculated as the peak 50 ms response bin across all stimuli presented.…”

Section: A1 Neurons Display a Sparse Code Across Some Acoustic Dimensmentioning

confidence: 91%

“…As an extension of these ideas, in a system where stimulus representation is progressively less linear as one ascends the processing hierarchy, at each stage the most efficient stimulus representation would be increasingly sparse. This hypothesis has been made in the visual (Willmore et al, 2011) and auditory (Wang, 2007;Atencio et al, 2012) systems.…”

Section: A1 Neurons Display a Sparse Code Across Some Acoustic Dimensmentioning

confidence: 99%

Online Stimulus Optimization Rapidly Reveals Multidimensional Selectivity in Auditory Cortical Neurons

Chambers

Hancock

Sen

et al. 2014

Journal of Neuroscience

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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 crossvalidated 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.

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“…This is because systematically covarying stimulus properties collapse into more efficient representations at the expense of separate redundant properties. Consistent with this principle, Wang (2007) describes "non-isomorphic" transformations that occur progressively along the ascending auditory pathway, making neural representations "further away from physical (acoustical) structures of sounds, but presumably closer to internal representations underlying perception" (p. 92). Examples of non-isomorphic representations in auditory cortex include encoding spectral shape across varying absolute frequencies (Barbour and Wang, 2003), gross representation of rapid change in click trains with short inter-click intervals versus phase-locking to trains with slower inter-click intervals (Lu and Wang, 2000;Lu et al, 2001), pitch versus individual frequency components Wang, 2005, 2006), and different components of auditory scenes (Nelken and Bar-Yosef, 2008).…”

Section: Introductionmentioning

confidence: 98%

Non-isomorphism in efficient coding of complex sound properties

Stilp

Kluender

2011

The Journal of the Acoustical Society of America

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To the extent that sensorineural systems are efficient, stimulus redundancy should be captured in ways that optimize information transmission. Consistent with this principle, neural representations of sounds have been proposed to become “non-isomorphic,” increasingly abstract and decreasingly resembling the original (redundant) input. Here, non-isomorphism is tested in perceptual learning using AXB discrimination of novel sounds with two highly correlated complex acoustic properties and a randomly varying third dimension. Discrimination of sounds obeying the correlation became superior to that of sounds violating it despite widely varying physical acoustic properties, suggesting non-isomorphic representation of stimulus redundancy.

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Neural coding strategies in auditory cortex

Cited by 117 publications

References 69 publications

Rapid efficient coding of correlated complex acoustic properties

Rapid efficient coding of correlated complex acoustic properties

Online Stimulus Optimization Rapidly Reveals Multidimensional Selectivity in Auditory Cortical Neurons

Non-isomorphism in efficient coding of complex sound properties

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