Functional Groups in the Avian Auditory System

Woolley, Sarah M. N.; Gill, Patrick R.; Fremouw, Thane; Theunissen, Frédéric E.

doi:10.1523/jneurosci.2042-08.2009

Cited by 92 publications

(140 citation statements)

References 72 publications

(88 reference statements)

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“…In the avian auditory system, STRFs that combine excitatory and inhibitory regions at the same time point (as shown in these two examples) appear only at the level of the cortex. 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%

“…These methodological advances have allowed auditory neuroscientists to make significant progress in understanding the nature of the auditory computations that are found in the ascending processing stream of both birds [30,41,48,51,[58][59][60][61] and mammals [38,[62][63][64][65]. Selectivity for natural sounds is already present at the level of the inferior colliculus (IC) in the sense that IC STRFs show temporal spectral features that are found in behaviorally relevant sounds [38,41,[66][67][68].…”

Section: Methods For Estimating Strfs Using Natural Soundsmentioning

confidence: 99%

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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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Section: Resultsmentioning

confidence: 99%

Section: Methods For Estimating Strfs Using Natural Soundsmentioning

confidence: 99%

Neural processing of natural sounds

2014

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“…The neural representation of songs-particularly in the primary auditory area field L Margoliash 1992, 1995;Vates et al 1996)-has been studied in detail Doupe 2006, 2008;Narayan et al 2006;Sen et al 2001;Theunissen et al 2000;Wang et al 2007;Woolley et al 2005Woolley et al , 2006Woolley et al , 2009. Most field L neurons are highly tuned in frequency, time, or both (Nagel and Doupe 2008;Woolley et al 2009).…”

Section: Introductionmentioning

confidence: 99%

“…Most field L neurons are highly tuned in frequency, time, or both (Nagel and Doupe 2008;Woolley et al 2009). In addition, field L responses change significantly with mean volume Doupe 2006, 2008).…”

Section: Introductionmentioning

confidence: 99%

Differential Influence of Frequency, Timing, and Intensity Cues in a Complex Acoustic Categorization Task

Nagel

McLendon

Doupe

2010

Journal of Neurophysiology

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Nagel KI, McLendon HM, Doupe AJ. Differential influence of frequency, timing, and intensity cues in a complex acoustic categorization task. J Neurophysiol 104: 1426 -1437, 2010. First published July 7, 2010 doi:10.1152/jn.00028.2010. Songbirds, which, like humans, learn complex vocalizations, provide an excellent model for the study of acoustic pattern recognition. Here we examined the role of three basic acoustic parameters in an ethologically relevant categorization task. Female zebra finches were first trained to classify songs as belonging to one of two males and then asked whether they could generalize this knowledge to songs systematically altered with respect to frequency, timing, or intensity. Birds' performance on song categorization fell off rapidly when songs were altered in frequency or intensity, but they generalized well to songs that were changed in duration by Ͼ25%. Birds were not deaf to timing changes, however; they detected these tempo alterations when asked to discriminate between the same song played back at two different speeds. In addition, when birds were retrained with songs at many intensities, they could correctly categorize songs over a wide range of volumes. Thus although they can detect all these cues, birds attend less to tempo than to frequency or intensity cues during song categorization. These results are unexpected for several reasons: zebra finches normally encounter a wide range of song volumes but most failed to generalize across volumes in this task; males produce only slight variations in tempo, but females generalized widely over changes in song duration; and all three acoustic parameters are critical for auditory neurons. Thus behavioral data place surprising constraints on the relationship between previous experience, behavioral task, neural responses, and perception. We discuss implications for models of auditory pattern recognition.

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“…Nonetheless, an expectation has emerged that speciesspecific spectral-temporal features of vocalizations will be reflected in species-specific auditory processing (Dooling et al, 2000;Woolley et al, 2009). We refer to this expectation as the sender-receiver matching hypothesis.…”

Section: Introductionmentioning

confidence: 99%

The sender-receiver matching hypothesis: support from the peripheral coding of acoustic features in songbirds

Gall

Brierley

Lucas

2012

Journal of Experimental Biology

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SUMMARYThe sender-receiver matching hypothesis predicts that species-specific features of vocalizations will be reflected in speciesspecific auditory processing. This hypothesis has most often been invoked to explain correlations between vocal frequency ranges and the frequency range of auditory sensitivity; however, it could apply to other structural features, such as the rise time of stimuli. We explored this hypothesis in five songbird species that vary in the rise times and frequency range of their vocalizations. We recorded auditory evoked potentials (AEPs) to onset and sustained portions of stimuli that varied in both frequency and rise time. AEPs are gross potentials generated in the auditory nerve and brainstem and measured from the scalp. We found that species with shorter rise times in their vocalizations had greater amplitude and shorter latency onset AEPs than species with longer rise times. We also found that species with lower frequency and/or more tonal vocalizations had stronger sustained AEPs that follow the sound pressure changes in the stimulus (i.e. frequency following responses) than species with higher frequency and/or less tonal vocalizations. This is the first study in songbirds to show that acoustic features such as rise time and tonality are reflected in peripheral auditory processing.

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Functional Groups in the Avian Auditory System

Cited by 92 publications

References 72 publications

Neural processing of natural sounds

Neural processing of natural sounds

Differential Influence of Frequency, Timing, and Intensity Cues in a Complex Acoustic Categorization Task

The sender-receiver matching hypothesis: support from the peripheral coding of acoustic features in songbirds

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