Biology subjects

Thoret, E.

Publications and source records attributed to Thoret, E..

8 recordsLinked to original sources

bioRxiv2025

Quantifying the effect of forest edge on tropical fauna using explainable ecoacoustics metrics

Tropical forests are biodiversity hotspots but increasingly fragmented by human activities, multiplying forest edges that alter microclimates and species dynamics. While edge effect on plants is well documented, its impact on fauna remains less ex-plored. Ecoacoustics provides a non-invasive and passive approach to monitor faunal communities, yet the complexity of soundscapes and the opacity of machine learning tools often limit ecological interpretability. Here, we show that acoustic diversity in New Caledonian ultramafic forests exhibits a detectable edge effect, with sound-scapes converging towards homogeneity starting 100 m from the forest edge. Using passive acoustic monitoring and 59 ecoacoustic indices, we combined machine learn-ing, Representational Similarity Analysis, and explainable statistical methods from psychophysics. Results reveal a significant correlation between distance to edge and acoustic diversity, particularly pronounced during day-time. Indices such as NDSI, Hf, and SKEWf contributed most to detecting the effect, whereas some other in-dices added noise rather than informative signal. These findings match the botanical threshold described by Blanchard et al. (2023) and highlight both the promise and limitations of indice-based ecoacoustics. By integrating explicability methods, we offer a transparent framework to disentangle complex ecological signals, advancing ecoacoustics as a pertinent tool for studying forest fragmentation and guiding biodi-versity conservation.

ecology↗

bioRxiv2024

Voice identity invariance by anterior temporal lobe neurons

The ability to recognize speakers by their voice despite acoustical variation plays a significant role in primate social interactions. While neurons in the macaque anterior temporal lobe (ATL) show invariance to face viewpoint, whether they also encode abstract representations of caller identity is not known. Here we show that neurons in the voice-selective ATL of two macaques show invariance to voice identity via dynamic representations that minimize within-caller neuronal distances while maintaining distinct neural trajectories for different individuals. A small proportion of highly identity-selective neurons plays a central role although less selective neurons are also informative. Our findings provide a neural basis for voice identity recognition in primates and highlight the ATL as a key hub for integrating perceptual voice features into higher-level identity representations.

neuroscience↗

bioRxiv2024

Reconstructing Voice Identity from Noninvasive Auditory Cortex Recordings

The cerebral processing of voice information is known to engage, in human as well as non-human primates, "temporal voice areas" (TVAs) that respond preferentially to conspecific vocalizations. However, how voice information is represented by neuronal populations in these areas, particularly speaker identity information, remains poorly understood. Here, we used a deep neural network (DNN) to generate a high-level, small-dimension representational space for voice identity--the voice latent space (VLS)--and examined its linear relation with cerebral activity via encoding, representational similarity, and decoding analyses. We find that the VLS maps onto fMRI measures of cerebral activity in response to tens of thousands of voice stimuli from hundreds of different speaker identities and better accounts for the representational geometry for speaker identity in the TVAs than in A1. Moreover, the VLS allowed TVA-based reconstructions of voice stimuli that preserved essential aspects of speaker identity as assessed by both machine classifiers and human listeners. These results indicate that the DNN-derived VLS provides high-level representations of voice identity information in the TVAs.

neuroscience↗

bioRxiv2023

Auditory hemispheric asymmetry as a specialization for actions and objects

What is the function of auditory hemispheric asymmetry? We propose that the identification of sound sources relies on two complementary and perceptually relevant acoustic invariants -- actions and objects -- that are processed asymmetrically and sufficient to model and categorize any sound. We observed that environmental sounds are an independent combination of purely temporal and spectral acoustic modulations. Behaviorally, discrimination of actions relied on temporal modulations, while discrimination of objects relied on spectral modulations. Functional magnetic resonance imaging data showed that actions and objects are respectively decoded in the left and right hemispheres, in bilateral superior temporal and left inferior frontal regions. This asymmetry reffects a generic differential processing -- through differential neural sensitivity to temporal and spectral modulations present in all environmental sounds -- that supports the efficient categorization of actions and objects. These results provide an ecologically valid framework of the functional role of auditory brain asymmetry.

neuroscience↗

bioRxiv2022

Sleep deprivation measured by voice analysis

Sleep deprivation has an ever-increasing impact on individuals and societies. Yet, to date, there is no quick and objective test for sleep deprivation. Here, we used automated acoustic analyses of the voice to detect sleep deprivation. Building on current machine-learning approaches, we focused on interpretability by introducing two novel ideas: the use of a fully generic auditory representation as input feature space, combined with an interpretation technique based on reverse correlation. The auditory representation consisted of a spectro-temporal modulation analysis derived from neurophysiology. The interpretation method aimed to reveal the regions of the auditory representation that supported the classifiers decisions. Results showed that generic auditory features could be used to detect sleep deprivation successfully, with an accuracy comparable to state-of-the-art speech features. Furthermore, the interpretation revealed two distinct effects of sleep deprivation on the voice: a change in prosody and a change in timbre. Importantly, the relative balance of the two effects varied widely across individuals, even though the amount of sleep deprivation was controlled, thus confirming the need to characterize sleep deprivation at the individual level. Moreover, while the prosody factor correlated with subjective sleepiness reports, the timbre factor did not, consistent with the presence of both explicit and implicit consequences of sleep deprivation. Overall, the findings show that individual effects of sleep deprivation may be observed in vocal biomarkers. Future investigations correlating such markers with objective physiological measures of sleep deprivation could enable "sleep stethoscopes" for the cost-effective diagnosis of the individual effects of sleep deprivation. Author summarySleep deprivation has an ever-increasing impact on individuals and societies, from accidents to chronic conditions costing billions to health systems. Yet, to date, there is no quick and objective test for sleep deprivation. We show that sleep deprivation can be detected at the individual level with voice recordings. Importantly, we focused on interpretability, which allowed us to identify two independent effects of sleep deprivation on the voice: a change in prosody and a change in timbre. The results also revealed a striking variability in individual reactions to the same deprivation, further confirming the need to consider the effects of sleep deprivation at the individual level. Vocal markers could be correlated to specific underlying physiological factors in future studies, outlining possible cost-effective and non-invasive "sleep stethoscopes".

neuroscience↗

bioRxiv2022

Hearing elliptic movements reveals the imprint of action on prototypical geometries

Within certain categories of geometric shapes, prototypical exemplars that best characterize the category have been evidenced. These geometric prototypes are classically identified through the visual and haptic perception or motor production and are usually characterized by their spatial dimension. However, whether prototypes can be recalled through the auditory channel has not been formally investigated. Here we address this question by using auditory cues issued from timbre-modulated friction sounds evoking human drawing elliptic movements. Since non-spatial auditory cues were previously found useful for discriminating distinct geometric shapes such as circles or ellipses, it is hypothesized that sound dynamics alone can evoke shapes such as an exemplary ellipse. Four experiments were conducted and altogether revealed that a common elliptic prototype emerges from auditory, visual, and motor modalities. This finding supports the hypothesis of a common coding of geometric shapes according to biological rules with a prominent role of sensory-motor contingencies in the emergence of such prototypical geometry.

neuroscience↗

bioRxiv2022

Tactile perception of auditory roughness

Auditory roughness resulting of fast temporal beatings is often studied by summing two pure tones with close frequencies. Interestingly, the tactile counterpart of auditory roughness can be provided through touch with vibrotactile actuators. However, whether auditory roughness could also be perceived through touch, and whether they exhibit similar characteristics is unclear. Here, auditory roughness perception and its tactile counterpart were evaluated using similar two pure tones stimuli. Results revealed similar roughness curves in both modalities suggesting similar sensory processing. This study attests of the relevance of such a paradigm for investing auditory and tactile roughness in a multisensory fashion.

neuroscience↗

bioRxiv2020

Probing machine-learning classifiers using noise, bubbles, and reverse correlation

BackgroundMany scientific fields now use machine-learning tools to assist with complex classification tasks. In neuroscience, automatic classifiers may be useful to diagnose medical images, monitor electrophysiological signals, or decode perceptual and cognitive states from neural signals. However, such tools often remain black-boxes: they lack interpretability. A lack of interpretability has obvious ethical implications for clinical applications, but it also limits the usefulness of these tools to formulate new theoretical hypotheses. New methodWe propose a simple and versatile method to help characterize the information used by a classifier to perform its task. Specifically, noisy versions of training samples or, when the training set is unavailable, custom-generated noisy samples, are fed to the classifier. Multiplicative noise, so-called "bubbles", or additive noise are applied to the input representation. Reverse correlation techniques are then adapted to extract either the discriminative information, defined as the parts of the input dataset that have the most weight in the classification decision, and represented information, which correspond to the input features most representative of each category. ResultsThe method is illustrated for the classification of written numbers by a convolutional deep neural network; for the classification of speech versus music by a support vector machine; and for the classification of sleep stages from neurophysiological recordings by a random forest classifier. In all cases, the features extracted are readily interpretable. Comparison with Existing MethodsQuantitative comparisons show that the present method can match state-of-the art interpretation methods for convolutional neural networks. Moreover, our method uses an intuitive and well-established framework in neuroscience, reverse correlation. It is also generic: it can be applied to any kind of classifier and any kind of input data. ConclusionsWe suggest that the method could provide an intuitive and versatile interface between neuroscientists and machine-learning tools. HighlightsO_LIThe heuristics of black-box classifiers can be probed with noisy inputs C_LIO_LIThe relevant features can be visualised in the input representation space C_LIO_LIThe method applies to any kind of data such as 2D images or 1D time series C_LIO_LIIt applies to any classifier such as deep neural networks, support vector machines, random forests C_LI

neuroscience↗