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:10.1101/2020.01.30.914747

Cited by 8 publications

(13 citation statements)

References 26 publications

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“…Sensory systems differentially encode environmental stimuli depending on the context in which they are encountered (De Franceschi & 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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“…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: 65%

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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“…Thus, since local features are not retained after averaging (McDermott, Schemitsch, and Simoncelli, 2013), it is possible to conclude that the two computations represent alternative processes, and the system switches from one to the other depending on the intrinsic sound properties (e.g., statistical variability) and the amount of available information in the auditory stream (e.g., increased stimulation length). Recent evidence in the animal model showed that auditory statistics ensembles are represented by distinct responses at the level of the midbrain (Zhai et al, 2020). These neural representations to auditory statistics occur at dissociable time scales, permitting the discrimination and recognition of natural sounds, with local information supporting sounds differentiation while the emergence of global structure their recognition (Zhai et al, 2020).…”

Section: Discussionmentioning

confidence: 99%

“…Recent evidence in the animal model showed that auditory statistics ensembles are represented by distinct responses at the level of the midbrain (Zhai et al, 2020). These neural representations to auditory statistics occur at dissociable time scales, permitting the discrimination and recognition of natural sounds, with local information supporting sounds differentiation while the emergence of global structure their recognition (Zhai et al, 2020). In our study, whole brain results in the source domain (Figure 3 Conversely, the same contrast at long durations linked the emergence of global discrimination predominantly to the left hemisphere: precisely, response peaked both around Heschl's gyrus, corresponding to early auditory cortex, and in the STS and STG; Figure 3A).…”

Section: Local and Global Discrimination Subtend To Alternative Proce...mentioning

confidence: 99%

Distinguishing fine structure and summary representation of sound textures from neural activity

Berto

Ricciardi

Pietrini

et al. 2022

Preprint

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The auditory system relies on local and global representations, depending on the amount of entering temporal details. When local information exceeds system constraints, acoustic details are summarized into a set of average statistics and global structure emerges. Whether computations subtending local and global representations of sounds can be distinguished at the cortical level is unknown. A computational auditory model was employed to extract auditory statistics from natural sound textures, such as fire, wind, and rain, and generate synthetic exemplars in which we had control of local and global properties. Participants were passively exposed to auditory streams with triplets of sounds including two standards and an oddball while the EEG was recorded. In Local Discrimination (exp 1) the oddball sound varied from standards only for its local features; in Global Discrimination (exp 2), it varied for its global structure. Different sound durations were employed to manipulate the amount of acoustic information and statistical variability between standards and oddballs. We expected short and long sounds to engage local (exp 1) or global (exp 2) representations, respectively. As predicted, neural responses associated with local discriminations exceeded global ones for short sounds, whereas the opposite pattern emerged for longer sounds. Electrical source estimates revealed that local discriminations mainly engaged auditory regions of the right hemisphere, while global discriminations of the left hemisphere. These findings revealed distinct neural correlates of acoustic discriminations guided by local or global structures. Ultimately results demonstrated that the auditory system developed a functional architecture to automatically detect changes occurring in the environment based on these two modes of sound representation.SIGNIFICANT STATEMENTPrior to this study, it was not known whether it was possible to directly measure auditory cortical representations of sound discriminations depending on local or global acoustic properties. Results unveiled the existence of a mechanisms which pre-attentively determines at which timescale (local or global) a change in a stream occurred based on statistical variability between sound excerpts. By performing source reconstruction, we additionally revealed that the response to local change detection pertains to different cortical structures as compared to global one, displaying also hemispheric asymmetries. These findings expand our knowledge on fundamental mechanisms characterizing auditory system functioning.

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

Cited by 8 publications

References 26 publications

Corticofugal regulation of predictive coding

Corticofugal regulation of predictive coding

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

Distinguishing fine structure and summary representation of sound textures from neural activity

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