Neural Modulation Tuning Characteristics Scale to Efficiently Encode Natural Sound Statistics

Rodríguez, Francisco Javier Álvarez; Chen, Chen; Read, Heather L.; Escabı́, Monty A.

doi:10.1523/jneurosci.0966-10.2010

Cited by 75 publications

(128 citation statements)

References 49 publications

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“…12 and 13), as predicted from previous single and multiunit studies (Atencio and Schreiner 2012;Cheung et al 2001;Eggermont et al 2011) and subcortical stations of the auditory pathway (Rodriguez et al 2010a(Rodriguez et al , 2010b. Our study builds on these prior studies because we demonstrated that reliable, phase-locked responses to sound features can be generated from ECoG-level signals.…”

Section: Discussionsupporting

confidence: 64%

A high-density, high-channel count, multiplexed μECoG array for auditory-cortex recordings

Escabı́

Read

Viventi

et al. 2014

Journal of Neurophysiology

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Section: Discussionsupporting

confidence: 64%

A high-density, high-channel count, multiplexed μECoG array for auditory-cortex recordings

Escabı́

Read

Viventi

et al. 2014

Journal of Neurophysiology

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“…Although the physical causes of the power-law relationship observed in natural images and in natural sounds are unrelated, it is highly probable that similar neural efficiency principles apply in both sensory systems. And indeed, a similar decorrelation has been observed in the inferior colliculus where the gain of auditory neurons emphasizes higher temporal and spectral modulation effectively counterbalancing the 1/ f relationship observed in the modulation power spectrum (and not the sound spectrum) of natural sounds [24]. At higher levels of the auditory processing stream, it has been shown that the population of neurons have a maximum gain at intermediate modulation frequencies, in a region that is particularly useful for distinguishing among different natural sounds [25].…”

Section: Perceptually Relevant Physical Characteristics Of Isolated Nsupporting

confidence: 56%

“…Additional avian STRF types and examples can be found in [51,58,59]. Examples in the mammalian auditory system can be found in [24,[62][63][64].…”

Section: Resultsmentioning

confidence: 99%

Neural processing of natural sounds

2014

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Natural sounds have unique statistical structure that makes them perceptually salient. The auditory system is finely tuned to this natural structure. Selective neurons found at the higher levels of the auditory system respond selectively to vocalizations used in communication. On-line Summary1. Natural sounds include animal vocalizations, environmental sounds such as wind, water and fire noises and non-vocal sounds made by animals and humans for communication. These natural sounds have characteristic statistical properties that make them perceptually salient and that drive auditory neurons in optimal regimes for information transmission. 2.Recent advances in statistics and computer sciences have allowed neuro-physiologists to extract the stimulus-response function of complex auditory neurons from responses to natural sounds. These studies have shown a hierarchical processing that leads to the neural detection of progressively more complex natural sound features and have demonstrated the importance of the acoustical and behavioral contexts for the neural responses.3. High-level auditory neurons have shown to be exquisitely selective for conspecific calls.This fine selectivity could play an important role for species recognition, for vocal learning in songbirds and, in the case of the bats, for the processing of the sounds used in echolocation. Research that investigates how communication sounds are categorized into behaviorally meaningful groups (e.g. call types in animals, words in human speech) remains in its infancy. Animals and humans also excel at separating communication sounds from each otherand from background noise. Neurons that detect communication calls in noise have been found but the neural computations involved in sound source separation and natural auditory scene analysis remain overall poorly understood. Thus, future auditory research will have to focus not only on how natural sounds are processed by the auditory system but also on the computations that allow for this processing to occur in natural listening situations. 5.The complexity of the computations needed in the natural hearing task might require a high-dimensional representation provided by ensemble of neurons and the use of natural sounds might be the best solution for understanding the ensemble neural code.The final publication is available at Nature via http://www.nature.com/nrn/journal/v15/n6/full/nrn3731.html PrefaceWe might be forced to listen to a high frequency tone at our audiologist's office or we might enjoy falling asleep with a white-noise machine but the sounds that really matter to us are the voices of our companions or music from our favorite radio station; the auditory system has evolved to process behaviorally relevant natural sounds. Research has shown not only that our brain is optimized for natural hearing tasks but also that using natural sounds to probe the auditory system is the best way to understand the neural computations that allow us to comprehend speech or appreciate music.

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“…We used both time and frequency representations to characterize the filtering performed on the envelope (Lesica and Grothe 2008b;Rodríguez et al 2010aRodríguez et al , 2010b. The time representation, called the MTRF, is the impulse response of the linear filter that relates the input amplitude modulation to the neuron's response (Fig.…”

Section: Resultsmentioning

confidence: 99%

Emergence of band-pass filtering through adaptive spiking in the owl's cochlear nucleus

Fontaine

MacLeod

Lubejko

et al. 2014

Journal of Neurophysiology

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In the visual, auditory, and electrosensory modalities, stimuli are defined by first-and second-order attributes. The fast time-pressure signal of a sound, a first-order attribute, is important, for instance, in sound localization and pitch perception, while its slow amplitude-modulated envelope, a second-order attribute, can be used for sound recognition. Ascending the auditory pathway from ear to midbrain, neurons increasingly show a preference for the envelope and are most sensitive to particular envelope modulation frequencies, a tuning considered important for encoding sound identity. The level at which this tuning property emerges along the pathway varies across species, and the mechanism of how this occurs is a matter of debate. In this paper, we target the transition between auditory nerve fibers and the cochlear nucleus angularis (NA). While the owl's auditory nerve fibers simultaneously encode the fast and slow attributes of a sound, one synapse further, NA neurons encode the envelope more efficiently than the auditory nerve. Using in vivo and in vitro electrophysiology and computational analysis, we show that a single-cell mechanism inducing spike threshold adaptation can explain the difference in neural filtering between the two areas. We show that spike threshold adaptation can explain the increased selectivity to modulation frequency, as input level increases in NA. These results demonstrate that a spike generation nonlinearity can modulate the tuning to second-order stimulus features, without invoking network or synaptic mechanisms.band-pass filtering; envelope encoding; cochlear nucleus; threshold adaptation SENSORY SYSTEMS HAVE EVOLVED to efficiently represent the statistics of natural stimuli (Barlow 1961). In the auditory system, the cochlea decomposes sound into narrow frequency channels. The output of every cochlear channel can be characterized by its first-order attribute, the time-dependent pressure wave or fine-structure, and second-order attribute, the instantaneous amplitude or envelope (Attias and Schreiner

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Neural Modulation Tuning Characteristics Scale to Efficiently Encode Natural Sound Statistics

Cited by 75 publications

References 49 publications

A high-density, high-channel count, multiplexed μECoG array for auditory-cortex recordings

A high-density, high-channel count, multiplexed μECoG array for auditory-cortex recordings

Neural processing of natural sounds

Emergence of band-pass filtering through adaptive spiking in the owl's cochlear nucleus

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