Stimulus-invariant processing and spectrotemporal reverse correlation in primary auditory cortex

Klein, David J.; Simon, Jonathan Z.; Depireux, Didier A.; Shamma, Shihab A.

doi:10.1007/s10827-005-3589-4

Cited by 55 publications

(75 citation statements)

References 62 publications

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“…More importantly, their analysis focused on gross-scale structure of the STRFs, whereas our results suggest that nonlinearities may express themselves in a more subtle manner. The DRC-and ripple-estimated STRFs of Figure 2, e and f, for instance, have a correlation coefficient of 0.83; this contrasts with the mean correlation coefficient of 0.64 for STRFs in the study by Klein et al (2006). Although the STRFs we estimate with DRC and ripple stimuli are well correlated, the differences between them would greatly complicate analysis of receptive field structure.…”

Section: Discussioncontrasting

confidence: 60%

“…Previously, for example, Klein et al (2006) examined the stability of STRFs estimated for auditory cortical neurons using a variety of stimulus classes and concluded that stimulus dependence of the STRFs was minimal (and therefore that the neuronal response functions could reasonably be described as linear). However, the stimuli they used were all derived from ripple stimuli and may possess similar higher-order statistics.…”

Section: Discussionmentioning

confidence: 99%

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The Consequences of Response Nonlinearities for Interpretation of Spectrotemporal Receptive Fields

Christianson

Sahani²,

Linden³

2008

J. Neurosci.

106

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Neurons in the central auditory system are often described by the spectrotemporal receptive field (STRF), conventionally defined as the best linear fit between the spectrogram of a sound and the spike rate it evokes. An STRF is often assumed to provide an estimate of the receptive field of a neuron, i.e., the spectral and temporal range of stimuli that affect the response. However, when the true stimulusresponse function is nonlinear, the STRF will be stimulus dependent, and changes in the stimulus properties can alter estimates of the sign and spectrotemporal extent of receptive field components. We demonstrate analytically and in simulations that, even when uncorrelated stimuli are used, interactions between simple neuronal nonlinearities and higher-order structure in the stimulus can produce STRFs that show contributions from time-frequency combinations to which the neuron is actually insensitive. Only when spectrotemporally independent stimuli are used does the STRF reliably indicate features of the underlying receptive field, and even then it provides only a conservative estimate. One consequence of these observations, illustrated using natural stimuli, is that a stimulus-induced change in an STRF could arise from a consistent but nonlinear neuronal response to stimulus ensembles with differing higher-order dependencies. Thus, although the responses of higher auditory neurons may well involve adaptation to the statistics of different stimulus ensembles, stimulus dependence of STRFs alone, or indeed of any overly constrained stimulus-response mapping, cannot demonstrate the nature or magnitude of such effects.

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Section: Discussioncontrasting

confidence: 60%

Section: Discussionmentioning

confidence: 99%

The Consequences of Response Nonlinearities for Interpretation of Spectrotemporal Receptive Fields

Christianson

Sahani²,

Linden³

2008

J. Neurosci.

106

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“…To explore possible neural mechanisms that could produce noise-robust representation in A1, we simulated neural activity using the spectrotemporal receptive field (STRF), which is commonly used to characterize functional properties of auditory neurons (10,19,20):…”

Section: Reduced Distortions In Stimuli Reconstructed From the A1 Popmentioning

confidence: 99%

Mechanisms of noise robust representation of speech in primary auditory cortex

Mesgarani

David

Fritz

et al. 2014

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

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126

155

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Humans and animals can reliably perceive behaviorally relevant sounds in noisy and reverberant environments, yet the neural mechanisms behind this phenomenon are largely unknown. To understand how neural circuits represent degraded auditory stimuli with additive and reverberant distortions, we compared single-neuron responses in ferret primary auditory cortex to speech and vocalizations in four conditions: clean, additive white and pink (1/f) noise, and reverberation. Despite substantial distortion, responses of neurons to the vocalization signal remained stable, maintaining the same statistical distribution in all conditions. Stimulus spectrograms reconstructed from population responses to the distorted stimuli resembled more the original clean than the distorted signals. To explore mechanisms contributing to this robustness, we simulated neural responses using several spectrotemporal receptive field models that incorporated either a static nonlinearity or subtractive synaptic depression and multiplicative gain normalization. The static model failed to suppress the distortions. A dynamic model incorporating feedforward synaptic depression could account for the reduction of additive noise, but only the combined model with feedback gain normalization was able to predict the effects across both additive and reverberant conditions. Thus, both mechanisms can contribute to the abilities of humans and animals to extract relevant sounds in diverse noisy environments.hearing | cortical | population code | phonemes V ocal communication in the real world often takes place in complex, noisy acoustic environments. Although substantial effort is required to perceive speech in extremely noisy conditions, accurate perception in moderately noisy and reverberant environments is relatively effortless (1), presumably because of the presence of general filtering mechanisms in the auditory pathway (2). These mechanisms likely influence the representation and perception of both speech and other natural sounds with similarly rich spectrotemporal structure, such as species-specific vocalizations (3-5). Despite the central role this robustness must play in animal and human hearing, little is known about the underlying neural mechanisms and whether the brain maintains invariant representations of these stimuli across variable soundscapes causing acoustic distortions of the original signals.Several theoretical and experimental studies have postulated that the distribution of linear spectrotemporal tuning of neurons found in the auditory pathway could support enhanced representation of temporal and spectral modulations matched to those prevalent in natural stimuli (6, 7). Others have attributed this effect to nonlinear response properties of neurons (8) and adaptation with various timescales (9). In this study, we tested the noise robustness of auditory cortical neurons by recording responses in ferret primary auditory cortex (A1) to natural vocalizations that were distorted by additive white and pink (1/f) noise or by convolutive reverber...

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“…The reliability of an STRF was measured using a bootstrap technique (Efron and Tibshirani, 1998), which allowed us to quantify the variance of the neural response and hence the overall SNR for each STRF. The bootstrap analysis, as well as SNR measurement procedure, are explained in details by Klein et al (2006). Most SNRs were Ͼ1, and we excluded all STRFs with an SNR Ͻ0.2 from our analysis.…”

Section: Receptive Field Estimationmentioning

confidence: 99%

Auditory Cortical Receptive Fields: Stable Entities with Plastic Abilities

Elhilali

Fritz

Chi³

et al. 2007

J. Neurosci.

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To form a reliable, consistent, and accurate representation of the acoustic scene, a reasonable conjecture is that cortical neurons maintain stable receptive fields after an early period of developmental plasticity. However, recent studies suggest that cortical neurons can be modified throughout adulthood and may change their response properties quite rapidly to reflect changing behavioral salience of certain sensory features. Because claims of adaptive receptive field plasticity could be confounded by intrinsic, labile properties of receptive fields themselves, we sought to gauge spontaneous changes in the responses of auditory cortical neurons. In the present study, we examined changes in a series of spectrotemporal receptive fields (STRFs) gathered from single neurons in successive recordings obtained over time scales of 30 -120 min in primary auditory cortex (A1) in the quiescent, awake ferret. We used a global analysis of STRF shape based on a large database of A1 receptive fields. By clustering this STRF space in a data-driven manner, STRF sequences could be classified as stable or labile. We found that Ͼ73% of A1 neurons exhibited stable receptive field attributes over these time scales. In addition, we found that the extent of intrinsic variation in STRFs during the quiescent state was insignificant compared with behaviorally induced STRF changes observed during performance of spectral auditory tasks. Our results confirm that task-related changes induced by attentional focus on specific acoustic features were indeed confined to behaviorally salient acoustic cues and could be convincingly attributed to learning-induced plasticity when compared with "spontaneous" receptive field variability.

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Stimulus-invariant processing and spectrotemporal reverse correlation in primary auditory cortex

Cited by 55 publications

References 62 publications

The Consequences of Response Nonlinearities for Interpretation of Spectrotemporal Receptive Fields

The Consequences of Response Nonlinearities for Interpretation of Spectrotemporal Receptive Fields

Mechanisms of noise robust representation of speech in primary auditory cortex

Auditory Cortical Receptive Fields: Stable Entities with Plastic Abilities

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