Distinct neural ensemble response statistics are associated with recognition and discrimination of natural sound textures

Xiu, Zhai; Khatami, Fatemeh; Sadeghi, Mina; He, Fengrong; Read, Heather L.; Stevenson, I. H.; Escabı́, Monty A.

doi:10.1073/pnas.2005644117

Cited by 20 publications

(15 citation statements)

References 30 publications

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“…Sensory systems differentially encode environmental stimuli depending on the context in which they are encountered ( De Franceschi and Barkat, 2020 ; Herrmann et al, 2015 ; Jaramillo et al, 2014 ; Pakan et al, 2016 ; Takesian et al, 2018 ; Zhai et al, 2020 ). The same physical stimulus can elicit distinct neuronal responses depending on whether it is predictable or unexpected in a given sensory stream ( Weissbart et al, 2020 ; Yaron et al, 2012 ).…”

Section: Introductionmentioning

confidence: 99%

Corticofugal regulation of predictive coding

Lesicko

Angeloni

Blackwell

et al. 2022

eLife

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Sensory systems must account for both contextual factors and prior experience to adaptively engage with the dynamic external environment. In the central auditory system, neurons modulate their responses to sounds based on statistical context. These response modulations can be understood through a hierarchical predictive coding lens: responses to repeated stimuli are progressively decreased, in a process known as repetition suppression, whereas unexpected stimuli produce a prediction error signal. Prediction error incrementally increases along the auditory hierarchy from the inferior colliculus (IC) to the auditory cortex (AC), suggesting that these regions may engage in hierarchical predictive coding. A potential substrate for top-down predictive cues is the massive set of descending projections from the auditory cortex to subcortical structures, although the role of this system in predictive processing has never been directly assessed. We tested the effect of optogenetic inactivation of the auditory cortico-collicular feedback in awake mice on responses of IC neurons to stimuli designed to test prediction error and repetition suppression. Inactivation of the cortico-collicular pathway led to a decrease in prediction error in IC. Repetition suppression was unaffected by cortico-collicular inactivation, suggesting that this metric may reflect fatigue of bottom-up sensory inputs rather than predictive processing. We also discovered populations of IC units that exhibit repetition enhancement, a sequential increase in firing with stimulus repetition. Cortico-collicular inactivation led to a decrease in repetition enhancement in the central nucleus of IC, suggesting that it is a top-down phenomenon. Negative prediction error, a stronger response to a tone in a predictable rather than unpredictable sequence, was suppressed in shell IC units during cortico-collicular inactivation. These changes in predictive coding metrics arose from bidirectional modulations in the response to the standard and deviant contexts, such that units in IC responded more similarly to each context in the absence of cortical input. We also investigated how these metrics compare between the anesthetized and awake states by recording from the same units under both conditions. We found that metrics of predictive coding and deviance detection differ depending on the anesthetic state of the animal, with negative prediction error emerging in the central IC and repetition enhancement and prediction error being more prevalent in the absence of anesthesia. Overall, our results demonstrate that the auditory cortex provides cues about the statistical context of sound to subcortical brain regions via direct feedback, regulating processing of both prediction and repetition.

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Section: Introductionmentioning

confidence: 99%

Corticofugal regulation of predictive coding

Lesicko

Angeloni

Blackwell

et al. 2022

eLife

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“…The laminar spacing and frequency bandwidths in auditory midbrain have been proposed to contribute to critical band perceptual resolution [54], and neural modulation bandwidths match those derived from perceptual measurements in humans [8,25,26]. Furthermore, decoding brain activity in auditory midbrain replicates perceptual trends for human texture perception[55]. Together, these results suggest that the mid-level auditory representation already contains spectro-temporal features that predict various aspects of natural sound perception.…”

Section: Discussionmentioning

confidence: 74%

Two stages of bandwidth scaling drives efficient neural coding of natural sounds

Stevenson

Escabı́

2022

Preprint

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Theories of efficient coding propose that the auditory system is optimized for the statistical structure of natural sounds, yet the transformations underlying optimal acoustic representations are not well understood. Using a database of natural sounds including human speech and a physiologically-inspired auditory model, we explore the consequences of peripheral (cochlear) and mid-level (auditory midbrain) filter tuning transformations on the representation of natural sound spectra and modulation statistics. Whereas Fourier-based spectrographic decompositions with equal resolution filters can preserve spectral details, cochlear filters with bandwidth scaling, sacrifice spectral information while producing a more robust temporal representation. Cochlear bandwidth scaling produces a frequency-dependent gain that counteracts the tendency of natural sound power to decrease with frequency, resulting in a whitened output representation. Mid-level auditory filter scaling further enhance the representation of natural sounds by producing a whitened modulation power spectrum (MPS) with higher modulation entropy than both the cochlear outputs and the conventional Fourier MPS. These findings suggest that the tuning characteristics of the peripheral and mid-level auditory system together produce a whitened output representation in three dimensions (frequency, temporal and spectral modulation) that reduces redundancies and allows for a more efficient use of neural resources. This hierarchical multi-stage tuning strategy is thus likely optimized to extract available information and may underlies perceptual sensitivity to natural sounds.

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“…However, there are no theories for how such cue interactions come about. One well-supported theory is that perception is strongly influenced by statistical variations of acoustical cues found in natural sounds (Elliott and Theunissen, 2009; Geffen et al, 2011; McDermott and Simoncelli, 2011; Zhai et al, 2020) Along these lines, we propose that statistical co-variations between onset and duration in natural sounds could explain why sound onsets strongly influence the perception of sound duration.…”

Section: Introductionmentioning

confidence: 63%

Correlations across timing cues in natural vocalizations predict biases in judging synthetic sound burst durations

Jané

Pisupati

Smith

et al. 2022

Preprint

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It is well known that animals rely on multiple sources of information in order to successfully identify sounds in natural environments, to make decisions that are optimal for their survival. For example, rats use duration and pitch cues to respond appropriately to prosocial and distress vocalizations (Saito et al., 2019). Vocalization duration cues are known to co-vary with other temporal cues (Khatami et al., 2018), yet little is known about whether animals rely upon such co-variations to successfully discriminate sounds. In the current study, we find natural alarm vocalizations in rats have onset and offset slopes that are correlated with their duration. Accordingly, vocalizations with faster onset slopes are more likely to have shorter durations. Given that vocalization slopes begin and end within milliseconds, they could provide rapid perceptual cues for predicting and discriminating vocalization duration. To examine this possibility, we train rodents to discriminate duration differences in sequences of synthetic vocalizations and examine how artificially changing the slope impacts duration judgments. We find animals are biased to misjudge a range of synthetic vocalizations as being shorter in duration when the onset and offset slopes are artificially fast. Moreover, this bias is reduced when rats are exposed to multiple synthetic vocalization bursts. The observed perceptual bias is accurately captured by a Bayesian decision-theoretic model that utilizes the empirical joint distribution of duration and onset slopes in natural vocalizations as a prior during duration judgements of synthetic vocalizations. This model also explains why the bias is reduced when more evidence is accumulated across multiple bursts, reducing the prior's influence. These results support the theory that animals are sensitive to fine-grained statistical co-variations in auditory timing cues and integrate this information optimally with incoming sensory evidence to guide their decisions.

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Distinct neural ensemble response statistics are associated with recognition and discrimination of natural sound textures

Cited by 20 publications

References 30 publications

Corticofugal regulation of predictive coding

Corticofugal regulation of predictive coding

Two stages of bandwidth scaling drives efficient neural coding of natural sounds

Correlations across timing cues in natural vocalizations predict biases in judging synthetic sound burst durations

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