Contrast Gain Control in Auditory Cortex

Rabinowitz, Neil C.; Willmore, Ben D. B.; Schnupp, Jan W. H.; King, Andrew J.

doi:10.1016/j.neuron.2011.04.030

Cited by 254 publications

(352 citation statements)

References 47 publications

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“…Instead, we found that a dynamic nonlinear model is necessary, accounting for both feed-forward, subtractive synaptic depression (21,27) and feedback, multiplicative gain normalization (14,15,28). Although the synaptic depression model alone can account partly for the suppression of additive noise, the combined depression/gain control model is necessary to replicate the neural data in more complex distortions such as reverberation.…”

Section: Discussionmentioning

confidence: 90%

“…Encoding models derived in clean signal conditions may not be sufficient to account for the encoding of spectrotemporally rich signals in complex noisy environments. A previous study of A1 that focused only on the representation of clean speech did not identify a functional role for divisive normalization (21), but studies with other noise stimuli have suggested that it might play a role (28). More generally, the relevance of environmental noise is well established in studies of neurophysiology (4), psychoacoustics (16), and automatic speech processing (29).…”

Section: Discussionmentioning

confidence: 99%

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Mechanisms of noise robust representation of speech in primary auditory cortex

Mesgarani

David

Fritz

et al. 2014

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

121

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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Section: Discussionmentioning

confidence: 90%

Section: Discussionmentioning

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.

121

155

View full text Add to dashboard Cite

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“…Results are reported as variance explained; this denotes the difference between the variance of the empirical distribution of CSD events, and the variance of the residuals obtained after subtracting the predicted distribution. We then improved these linear models by extending them to linear-nonlinear models (Chichilnisky, 2001;Simoncelli et al, 2004;Rabinowitz et al, 2011). This captures additional nonlinearities (such as thresholding) in the relationship between stimulus and CSD events, by passing the output of the STRF through a static nonlinearity.…”

Section: Spectrotemporal Receptive Field Estimationmentioning

confidence: 99%

The Laminar and Temporal Structure of Stimulus Information in the Phase of Field Potentials of Auditory Cortex

et al. 2011

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Recent studies have shown that the phase of low-frequency local field potentials (LFPs) in sensory cortices carries a significant amount of information about complex naturalistic stimuli, yet the laminar circuit mechanisms and the aspects of stimulus dynamics responsible for generating this phase information remain essentially unknown. Here we investigated these issues by means of an information theoretic analysis of LFPs and current source densities (CSDs) recorded with laminar multi-electrode arrays in the primary auditory area of anesthetized rats during complex acoustic stimulation (music and broadband 1/f stimuli). We found that most LFP phase information originated from discrete "CSD events" consisting of granular-superficial layer dipoles of short duration and large amplitude, which we hypothesize to be triggered by transient thalamocortical activation. These CSD events occurred at rates of 2-4 Hz during both stimulation with complex sounds and silence. During stimulation with complex sounds, these events reliably reset the LFP phases at specific times during the stimulation history. These facts suggest that the informativeness of LFP phase in rat auditory cortex is the result of transient, large-amplitude events, of the "evoked" or "driving" type, reflecting strong depolarization in thalamo-recipient layers of cortex. Finally, the CSD events were characterized by a small number of discrete types of infragranular activation. The extent to which infragranular regions were activated was stimulus dependent. These patterns of infragranular activations may reflect a categorical evaluation of stimulus episodes by the local circuit to determine whether to pass on stimulus information through the output layers.

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“…The presence of multiple auditory cortical areas on the ectosylvian gyrus (EG) of this species was first demonstrated by using 2‐deoxyglucose autoradiography (Wallace et al, 1997) and subsequently confirmed by using optical imaging of intrinsic signals (Nelken et al, 2004) and single‐unit recording (Kelly et al, 1986; Kelly and Judge, 1994; Kowalski et al, 1995; Bizley et al, 2005). Although most electrophysiological recording studies have focused on the primary auditory cortex (A1) (Phillips et al, 1988; Kowalski et al, 1996; Schnupp et al, 2001; Fritz et al, 2003; Rabinowitz et al, 2011; Keating et al, 2013), the nonprimary auditory fields in this species are now receiving increasing attention (Nelken et al, 2008; Bizley et al, 2009, 2010, 2013; Walker et al, 2011; Atiani et al, 2014). …”

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confidence: 99%

Cortico‐cortical connectivity within ferret auditory cortex

Bizley

Bajo

Nodal³

et al. 2015

J of Comparative Neurology

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Despite numerous studies of auditory cortical processing in the ferret (Mustela putorius), very little is known about the connections between the different regions of the auditory cortex that have been characterized cytoarchitectonically and physiologically. We examined the distribution of retrograde and anterograde labeling after injecting tracers into one or more regions of ferret auditory cortex. Injections of different tracers at frequency‐matched locations in the core areas, the primary auditory cortex (A1) and anterior auditory field (AAF), of the same animal revealed the presence of reciprocal connections with overlapping projections to and from discrete regions within the posterior pseudosylvian and suprasylvian fields (PPF and PSF), suggesting that these connections are frequency specific. In contrast, projections from the primary areas to the anterior dorsal field (ADF) on the anterior ectosylvian gyrus were scattered and non‐overlapping, consistent with the non‐tonotopic organization of this field. The relative strength of the projections originating in each of the primary fields differed, with A1 predominantly targeting the posterior bank fields PPF and PSF, which in turn project to the ventral posterior field, whereas AAF projects more heavily to the ADF, which then projects to the anteroventral field and the pseudosylvian sulcal cortex. These findings suggest that parallel anterior and posterior processing networks may exist, although the connections between different areas often overlap and interactions were present at all levels. J. Comp. Neurol. 523:2187–2210, 2015. © 2015 Wiley Periodicals, Inc.

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Contrast Gain Control in Auditory Cortex

Cited by 254 publications

References 47 publications

Mechanisms of noise robust representation of speech in primary auditory cortex

Mechanisms of noise robust representation of speech in primary auditory cortex

The Laminar and Temporal Structure of Stimulus Information in the Phase of Field Potentials of Auditory Cortex

Cortico‐cortical connectivity within ferret auditory cortex

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