Neural population coding of sound level adapts to stimulus statistics

Dean, Isabel; Harper, Nicol S.; McAlpine, David

doi:10.1038/nn1541

Cited by 466 publications

(516 citation statements)

References 35 publications

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“…Speed sensitivity increased for some stimuli but decreased for others. Similar neuronal changes were found in other visual submodalities (34) and other sensory modalities (35).…”

Section: Discussionsupporting

confidence: 79%

Sensory adaptation as optimal resource allocation

Gepshtein

Lesmes

Albright

2013

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

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Fig. 1. The phylogenetic distribution of ABO phenotypes and genotypes. Shown is a phylogenetic tree of primate species, with a summary of phenotypic/ genotypic information given in the first column, and the genetic basis for the A versus B phenotype provided in the second column (functionally important codons at positions 266 and 268 are in uppercase letters). See Dataset S1 for the source of information about phenotypes/genotypes. Only species with available divergence times are represented here (34 of 40). The phylogenetic tree is drawn to scale, with divergence times (on the x axis) in millions of years taken from ref. 29. OWM, Old World monkeys; NWM, New World monkeys. Under a model of convergent evolution, these data suggest that A is the ancestral allele, and a turnover (e.g., a neutral substitution) occurred on the branch leading to Old World monkeys. If instead, B were ancestral, all Old World monkeys would have had to serendipitously converge from ATG to TTG to encode a leucine, whereas all New World monkeys and hominoids would have had to converge to the CTG codon.

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“…Speed sensitivity increased for some stimuli but decreased for others. Similar neuronal changes were found in other visual submodalities (34) and other sensory modalities (35).…”

Section: Discussionsupporting

confidence: 79%

Sensory adaptation as optimal resource allocation

Gepshtein

Lesmes

Albright

2013

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

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“…Moreover, although the frequency of bursting varies across stimuli, the high accuracy of feature detection remains constant. This stimulusdependent change in the response properties of AN2 is functionally similar to the adaptation that has been described in a number of sensory systems (Smirnakis et al, 1997;Kvale and Schreiner, 2004;Dean et al, 2005), which allows accurate detection and encoding of changes in a stimulus over a wide range of stimulus statistics.…”

Section: Discussionsupporting

confidence: 52%

A Behavioral Role for Feature Detection by Sensory Bursts

Marsat¹,

Pollack²

2006

J. Neurosci.

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Brief episodes of high-frequency firing of sensory neurons, or bursts, occur in many systems, including mammalian auditory and visual systems, and are believed to signal the occurrence of particularly important stimulus features, i.e., to function as feature detectors. However, the behavioral relevance of sensory bursts has not been established in any system. Here, we show that bursts in an identified auditory interneuron of crickets reliably signal salient stimulus features and reliably predict behavioral responses. Our results thus demonstrate the close link between sensory bursts and behavior.

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“…The puzzling observation that this dynamic range decreases dramatically in noise has been called the "dynamic range problem" because behavioral results show that intensity changes are detectable up to high levels or in noise (e.g., Viemeister 1988). Recent data have shown that there may be dynamic range adaptation based on average sound intensity at various stages of the auditory pathway (Dean et al 2005;Wen et al 2009). The MOCR may be a mechanism contributing to this adaptation at an early stage in the auditory periphery.…”

Section: Bmentioning

confidence: 99%

“…This wide range occurs despite the fact that individual auditory neurons can change firing rates only over a limited range of levels. One way in which the auditory system may be able to accomplish this is through dynamic range adaptation (Dean et al 2005), which effectively shifts the dynamic range of neurons based on preceding acoustic stimulation. Some dynamic range adaptation may be produced by the medial olivocochlear reflex (MOCR) .…”

Section: Introductionmentioning

confidence: 99%

Modeling the Anti-masking Effects of the Olivocochlear Reflex in Auditory Nerve Responses to Tones in Sustained Noise

Chintanpalli

Jennings

Heinz

et al. 2012

JARO

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The medial olivocochlear reflex (MOCR) has been hypothesized to provide benefit for listening in noise. Strong physiological support for an anti-masking role for the MOCR has come from the observation that auditory nerve (AN) fibers exhibit reduced firing to sustained noise and increased sensitivity to tones when the MOCR is elicited. The present study extended a well-established computational model for normal-hearing and hearing-impaired AN responses to demonstrate that these anti-masking effects can be accounted for by reducing outer hair cell (OHC) gain, which is a primary effect of the MOCR. Tone responses in noise were examined systematically as a function of tone level, noise level, and OHC gain. Signal detection theory was used to predict detection and discrimination for different spontaneous rate fiber groups. Decreasing OHC gain decreased the sustained noise response and increased maximum discharge rate to the tone, thus modeling the ability of the MOCR to decompress AN fiber rate-level functions. Comparing the present modeling results with previous data from AN fibers in decerebrate cats suggests that the ipsilateral masking noise used in the physiological study may have elicited up to 20 dB of OHC gain reduction in addition to that inferred from the contralateral noise effects. Reducing OHC gain in the model also extended the dynamic range for discrimination over a wide range of background noise levels. For each masker level, an optimal OHC gain reduction was predicted (i.e., where maximum discrimination was achieved without increased detection threshold). These optimal gain reductions increased with masker level and were physiologically realistic. Thus, reducing OHC gain can improve tone-in-noise discrimination even though it may produce a "hearing loss" in quiet. Combining MOCR effects with the sensorineural hearing loss effects already captured by this computational AN model will be beneficial for exploring the implications of their interaction for the difficulties hearing-impaired listeners have in noisy situations.

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Neural population coding of sound level adapts to stimulus statistics

Cited by 466 publications

References 35 publications

Sensory adaptation as optimal resource allocation

Sensory adaptation as optimal resource allocation

A Behavioral Role for Feature Detection by Sensory Bursts

Modeling the Anti-masking Effects of the Olivocochlear Reflex in Auditory Nerve Responses to Tones in Sustained Noise

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