Biology subjects

Escabi, M. A.

Publications and source records attributed to Escabi, M. A..

6 recordsLinked to original sources

bioRxiv2026

Noise statistics drive background- and channel-specific interference in auditory midbrain representations of speech

Although humans and animals excel at interpreting target sounds in competing noise, sound recognition abilities can vary widely due to statistical characteristics of the interfering background. Yet, how the brain leverages statistical sound information to segregate sounds in noise remains unclear. Here, using many natural auditory textures as background stimuli, we test how the encoding of speech is altered by natural noises in the inferior colliculus (IC) of unanesthetized rabbits. We identify foreground- and background-driven neural response components to sound mixtures and find that the background statistics alter the neural representation of speech in a frequency- and modulation-specific manner. Neural encoding signal-to-noise ratios (SNRs) vary extensively across background categories, frequencies, and modulations, and these influence the encoding of speech independently of the acoustic SNR. This variability is driven by both the background spectrum and modulation statistics, which distort the speech representation and show distinct interference effects in the modulation ranges for rhythm and pitch. Thus, spectrum and modulation statistics critical to speech recognition in noise are reflected in IC neural population activity. These neural response statistics and resulting distortions likely determine the coding fidelity for speech in downstream cortical regions and provide a neural basis for differences in speech intelligibility in real-world noises.

neuroscience↗

bioRxiv2026

Modulation statistics allow robust prediction of speech recognition accuracy across many words, voices, and natural background sounds.

Although humans excel at speech recognition, recognition accuracy can vary widely due to differences in background environments as well as the speakers voice quality, intonation, and pitch. Predicting when speech recognition will succeed or fail, however, remains an ongoing challenge in hearing research. Here we characterize recognition abilities across a wide range of natural conditions using digits spoken by many male and female talkers of multiple ages with 33 unique backgrounds. Across this diverse set of sounds, speech recognition is most strongly influenced by the spectrum and modulation statistics of the noise. Yet, articulatory features of the speech, including fundamental and formant frequencies, show categorically distinct modulatory effects on accuracy across age, gender, and words. We then show that a low-dimensional model of sound, based on computations in the auditory midbrain, accounts for participants single-trial recognition behavior across voices, words and backgrounds. Thus, speech-in-noise perception across extremely diverse natural conditions depends largely on a simple set of spectrotemporal statistics likely encoded by central neural populations.

neuroscience↗

bioRxiv2025

Parametric models for predicting nonstationary spike-spike correlations with local field potentials

ObjectiveCorrelations between the spiking of pairs of neurons are often used to study the brains representation of sensory or motor variables and neural circuit function and dysfunction. Previous statistical techniques have shown how time-averaged spike-spike correlations can be predicted by the time-averaged relationships between the individual neurons and the local field potential (LFP). However, spiking and LFP are both nonstationary, and spike-spike correlations have nonstationary structure that cannot be accounted for by time-averaged approaches. Here our goal is to develop parametric models that predict spike-spike correlations using a small number of LFP-based predictors and apply these models to the problem of tracking changes in spike-spike correlations over time. ApproachParametric models allow for flexibility in the choice of which LFP recording channels and frequency bands to use for prediction, and coefficients directly indicate which LFP features drive correlated spiking. Here we demonstrate our methods in simulation and test the models on experimental data from large-scale multi-electrode recordings in the mouse hippocampus and visual cortex. Main resultsIn single time windows, we find that our parametric models can be as accurate as previous nonparametric approaches, while also being flexible and interpretable. We then demonstrate how parametric models can be applied to describe nonstationary spike-spike correlations measured in sequential time windows. We find that although the patterns of both cortical and hippocampal spike-spike correlations vary over time, these changes are, at least partially, predicted by models that assume a fixed spike-field relationship. SignificanceThis approach may thus help to better understand how the dynamics of spike-spike correlations are related to functional brain states. Since spike-spike correlations are increasingly used as features for decoding external variables from neural activity, these models may also have the potential to improve the accuracy of adaptive decoders and brain machine interfaces.

neuroscience↗

bioRxiv2024

Low-dimensional interference of mid-level sound statistics predicts human speech recognition in natural environmental noise

Recognizing speech in noise, such as in a busy restaurant, is an essential cognitive skill where the task difficulty varies across environments and noise levels. Although there is growing evidence that the auditory system relies on statistical representations for perceiving 1-5 and coding4,6-9 natural sounds, its less clear how statistical cues and neural representations contribute to segregating speech in natural auditory scenes. We demonstrate that human listeners rely on mid-level statistics to segregate and recognize speech in environmental noise. Using natural backgrounds and variants with perturbed spectro-temporal statistics, we show that speech recognition accuracy at a fixed noise level varies extensively across natural backgrounds (0% to 100%). Furthermore, for each background the unique interference created by summary statistics can mask or unmask speech, thus hindering or improving speech recognition. To identify the neural coding strategy and statistical cues that influence accuracy, we developed generalized perceptual regression, a framework that links summary statistics from a neural model to word recognition accuracy. Whereas a peripheral cochlear model accounts for only 60% of perceptual variance, summary statistics from a mid-level auditory midbrain model accurately predicts single trial sensory judgments, accounting for more than 90% of the perceptual variance. Furthermore, perceptual weights from the regression framework identify which statistics and tuned neural filters are influential and how they impact recognition. Thus, perception of speech in natural backgrounds relies on a mid-level auditory representation involving interference of multiple summary statistics that impact recognition beneficially or detrimentally across natural background sounds. Significance StatementRecognizing speech in natural auditory scenes with competing talkers and environmental noise is a critical cognitive skill. Although normal listeners effortlessly perform this task, for instance in a crowded restaurant, it challenges individuals with hearing loss and our most sophisticated machine systems. We tested human participants listening to speech in natural noises with varied statistical characteristics and demonstrate that they rely on a statistical representation of sounds to segregate speech from environmental noise. Using a model of the auditory system, we then demonstrate that a brain inspired statistical representation of natural sounds accurately predicts human perceptual trends across wide range of natural backgrounds and noise levels and reveals key statistical features and neural computations underlying human abilities for this task.

neuroscience↗

bioRxiv2022

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

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.

neuroscience↗

bioRxiv2018

Spiking network optimized for noise robust word recognition approaches human-level performance and predicts auditory system hierarchy

The auditory neural code is resilient to acoustic variability and capable of recognizing sounds amongst competing sound sources, yet, the transformations enabling noise robust abilities are largely unknown. We report that a hierarchical spiking neural network (HSNN) trained to maximize word recognition accuracy in noise and multiple talkers approaches near human-level performance. Intriguingly, comparisons with data from auditory nerve, midbrain, thalamus and cortex reveals that the organization and nonlinear transformations of the optimal network predict several properties of the ascending auditory pathway including a sequential loss of temporal resolution, increasing sparseness and selectivity. The optimal organizational scheme is critical for noise robustness since an identical network arranged to enable high information transfer does not predict auditory pathway organization and has substantially poorer performance. Furthermore, conventional linear and nonlinear receptive field-based models fail to achieve similar noise robust performance. The findings suggest that the auditory pathway hierarchy and its sequential nonlinear feature extraction computations may form a near optimal code capable of efficiently detecting sounds in noise impoverished conditions.

neuroscience↗