Functional Convergence of Response Properties in the Auditory Thalamocortical System

Miller, Lee M.; Escabı́, Monty A.; Read, Heather L.; Schreiner, Christoph E.

doi:10.1016/s0896-6273(01)00445-7

Cited by 214 publications

(150 citation statements)

References 53 publications

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“…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%

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: Methods For Estimating Strfs Using Natural Soundsmentioning

confidence: 99%

Neural processing of natural sounds

2014

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“…The lack of such sensitivity in auditory thalamus, for the same stimulus parameters for which it was clearly present in A1, implies a function that is unique to A1. Many years of research have indicated that the performance of A1 neurons is not better than, and is probably even worse than, subcortical neurons, when comparing standard measures of sensory coding such as width of tuning curves and temporal response properties (Creutzfeldt et al, 1980;Miller et al, 2001;Joris et al, 2004). Therefore, we propose that we need to consider higher-level functions, such as sensory memory, if we want to understand the role of A1 in auditory processing.…”

Section: Mechanisms Of Adaptationmentioning

confidence: 99%

Multiple Time Scales of Adaptation in Auditory Cortex Neurons

Ulanovsky¹,

Las²,

Farkas³

et al. 2004

J. Neurosci.

660

671

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Neurons in primary auditory cortex (A1) of cats show strong stimulus-specific adaptation (SSA). In probabilistic settings, in which one stimulus is common and another is rare, responses to common sounds adapt more strongly than responses to rare sounds. This SSA could be a correlate of auditory sensory memory at the level of single A1 neurons. Here we studied adaptation in A1 neurons, using three different probabilistic designs. We showed that SSA has several time scales concurrently, spanning many orders of magnitude, from hundreds of milliseconds to tens of seconds. Similar time scales are known for the auditory memory span of humans, as measured both psychophysically and using evoked potentials. A simple model, with linear dependence on both short-term and long-term stimulus history, provided a good fit to A1 responses. Auditory thalamus neurons did not show SSA, and their responses were poorly fitted by the same model. In addition, SSA increased the proportion of failures in the responses of A1 neurons to the adapting stimulus. Finally, SSA caused a bias in the neuronal responses to unbiased stimuli, enhancing the responses to eccentric stimuli. Therefore, we propose that a major function of SSA in A1 neurons is to encode auditory sensory memory on multiple time scales. This SSA might play a role in stream segregation and in binding of auditory objects over many time scales, a property that is crucial for processing of natural auditory scenes in cats and of speech and music in humans.

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“…It has been suggested that certain synaptic properties and connectivity patterns of PAC are specific for extracting spectrotemporal feature information [13][14][15] . Hence, information processing of visual system and that of the auditory system may be quite different.…”

Section: Visual "What" and "Where" Segregation Pathwaysmentioning

confidence: 99%

The dual-pathway model of auditory signal processing

Wang

2008

Neurosci. Bull.

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Similar to the visual dual-pathway model, neurophysiological studies in non-human primates have suggested that the dual-pathway model is also applicable for explaining auditory cortical processing, including the ventral "what" pathway for object identification and the dorsal "where" pathway for spatial localization. This review summarizes evidence from human neuroimaging studies supporting the dual-pathway model for auditory cortical processing in humans.

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Functional Convergence of Response Properties in the Auditory Thalamocortical System

Cited by 214 publications

References 53 publications

Neural processing of natural sounds

Neural processing of natural sounds

Multiple Time Scales of Adaptation in Auditory Cortex Neurons

The dual-pathway model of auditory signal processing

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