scholarly journals Neural Coding of Noisy and Reverberant Speech in Human Auditory Cortex

2017 ◽  
Author(s):  
Krishna C Puvvada ◽  
Marisel Villafañe-Delgado ◽  
Christian Brodbeck ◽  
Jonathan Z Simon
Keyword(s):  
Neural Coding ◽  
Human Subjects ◽  
Neural Representation ◽  
Speech Communication ◽  
Specific Information ◽  
Neural Encoding ◽  
Multiple Reflections ◽  
Noise Sources ◽  
Listening Environments ◽  
Stimulus Reconstruction

AbstractSpeech communication in daily listening environments is complicated by the phenomenon of reverberation, wherein any sound reaching the ear is a mixture of the direct component from the source and multiple reflections off surrounding objects and the environment. The brain plays a central role in comprehending speech accompanied by such distortion, which, frequently, is further complicated by the presence of additional noise sources in the vicinity. Here, using magnetoencephalography (MEG) recordings from human subjects, we investigate the neural representation of speech in noisy, reverberant listening conditions as measured by phase-locked MEG responses to the slow temporal modulations of speech. Using systems-theoretic linear methods of stimulus encoding, we observe that the cortex maintains both distorted and distortion-free (cleaned) representations of speech. Also, we show that, while neural encoding of speech remains robust to additive noise in absence of reverberation, it is detrimentally affected by noise when present along with reverberation. Further, using linear methods of stimulus reconstruction, we show that theta-band neural responses are a likely candidate for the distortion free representation of speech, whereas delta band responses are more likely to carry non-speech specific information regarding the listening environment.

scholarly journals Cortical Representations of Speech in a Multi-talker Auditory Scene

2017 ◽  
Author(s):  
Krishna C. Puvvada ◽  
Jonathan Z. Simon
Keyword(s):  
Auditory Cortex ◽  
Human Subjects ◽  
Higher Order ◽  
Neural Representation ◽  
Cortical Areas ◽  
Auditory Scene ◽  
Global Representation ◽  
Stimulus Reconstruction ◽  
Background Object ◽  
Cortical Representations

AbstractThe ability to parse a complex auditory scene into perceptual objects is facilitated by a hierarchical auditory system. Successive stages in the hierarchy transform an auditory scene of multiple overlapping sources, from peripheral tonotopically-based representations in the auditory nerve, into perceptually distinct auditory-objects based representation in auditory cortex. Here, using magnetoencephalography (MEG) recordings from human subjects, both men and women, we investigate how a complex acoustic scene consisting of multiple speech sources is represented in distinct hierarchical stages of auditory cortex. Using systems-theoretic methods of stimulus reconstruction, we show that the primary-like areas in auditory cortex contain dominantly spectro-temporal based representations of the entire auditory scene. Here, both attended and ignored speech streams are represented with almost equal fidelity, and a global representation of the full auditory scene with all its streams is a better candidate neural representation than that of individual streams being represented separately. In contrast, we also show that higher order auditory cortical areas represent the attended stream separately, and with significantly higher fidelity, than unattended streams. Furthermore, the unattended background streams are more faithfully represented as a single unsegregated background object rather than as separated objects. Taken together, these findings demonstrate the progression of the representations and processing of a complex acoustic scene up through the hierarchy of human auditory cortex.Significance StatementUsing magnetoencephalography (MEG) recordings from human listeners in a simulated cocktail party environment, we investigate how a complex acoustic scene consisting of multiple speech sources is represented in separate hierarchical stages of auditory cortex. We show that the primary-like areas in auditory cortex use a dominantly spectro-temporal based representation of the entire auditory scene, with both attended and ignored speech streams represented with almost equal fidelity. In contrast, we show that higher order auditory cortical areas represent an attended speech stream separately from, and with significantly higher fidelity than, unattended speech streams. Furthermore, the unattended background streams are represented as a single undivided background object rather than as distinct background objects.

scholarly journals Concurrent Encoding of Frequency and Amplitude Modulation in Human Auditory Cortex: Encoding Transition

2007 ◽  
Vol 98 (6) ◽  
pp. 3473-3485 ◽  
Author(s):  
Huan Luo ◽  
Yadong Wang ◽  
David Poeppel ◽  
Jonathan Z. Simon
Keyword(s):  
Phase Modulation ◽  
Neural Coding ◽  
Human Subjects ◽  
Acoustic Properties ◽  
Acoustic Features ◽  
Neural Encoding ◽  
State Response ◽  
Auditory Steady State Response ◽  
Animal Vocalizations ◽  
Physiological Tests

Complex natural sounds (e.g., animal vocalizations or speech) can be characterized by specific spectrotemporal patterns the components of which change in both frequency (FM) and amplitude (AM). The neural coding of AM and FM has been widely studied in humans and animals but typically with either pure AM or pure FM stimuli. The neural mechanisms employed to perceptually unify AM and FM acoustic features remain unclear. Using stimuli with simultaneous sinusoidal AM (at rate fAM = 37 Hz) and FM (with varying rates ƒFM), magnetoencephalography (MEG) is used to investigate the elicited auditory steady-state response (aSSR) at relevant frequencies (ƒAM, ƒFM, ƒAM + fFM). Previous work demonstrated that for sounds with slower FM dynamics ( fFM < 5 Hz), the phase of the aSSR at ƒAM tracked the FM; in other words, AM and FM features were co-tracked and co-represented by “phase modulation” encoding. This study explores the neural coding mechanism for stimuli with faster FM dynamics (≤30 Hz), demonstrating that at faster rates ( fFM > 5 Hz), there is a transition from pure phase modulation encoding to a single-upper-sideband (SSB) response (at frequency fAM + fFM) pattern. We propose that this unexpected SSB response can be explained by the additional involvement of subsidiary AM encoding responses simultaneously to, and in quadrature with, the ongoing phase modulation. These results, using MEG to reveal a possible neural encoding of specific acoustic properties, demonstrate more generally that physiological tests of encoding hypotheses can be performed noninvasively on human subjects, complementing invasive, single-unit recordings in animals.

scholarly journals EEG-based diagnostics of the auditory system using cochlear implant electrodes as sensors

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ben Somers ◽  
Christopher J. Long ◽  
Tom Francart
Keyword(s):  
Cochlear Implant ◽  
Auditory Evoked Potentials ◽  
Human Subjects ◽  
Electrode Array ◽  
Brain Regions ◽  
Hearing Impaired ◽  
Brain Responses ◽  
Listening Environments ◽  
Set Up ◽  
Neural Feedback

AbstractThe cochlear implant is one of the most successful medical prostheses, allowing deaf and severely hearing-impaired persons to hear again by electrically stimulating the auditory nerve. A trained audiologist adjusts the stimulation settings for good speech understanding, known as “fitting” the implant. This process is based on subjective feedback from the user, making it time-consuming and challenging, especially in paediatric or communication-impaired populations. Furthermore, fittings only happen during infrequent sessions at a clinic, and therefore cannot take into account variable factors that affect the user’s hearing, such as physiological changes and different listening environments. Objective audiometry, in which brain responses evoked by auditory stimulation are collected and analysed, removes the need for active patient participation. However, recording of brain responses still requires expensive equipment that is cumbersome to use. An elegant solution is to record the neural signals using the implant itself. We demonstrate for the first time the recording of continuous electroencephalographic (EEG) signals from the implanted intracochlear electrode array in human subjects, using auditory evoked potentials originating from different brain regions. This was done using a temporary recording set-up with a percutaneous connector used for research purposes. Furthermore, we show that the response morphologies and amplitudes depend crucially on the recording electrode configuration. The integration of an EEG system into cochlear implants paves the way towards chronic neuro-monitoring of hearing-impaired patients in their everyday environment, and neuro-steered hearing prostheses, which can autonomously adjust their output based on neural feedback.

scholarly journals Duplicate Detection of Spike Events: A Relevant Problem in Human Single-Unit Recordings

2021 ◽  
Vol 11 (6) ◽  
pp. 761
Author(s):  
Gert Dehnen ◽  
Marcel S. Kehl ◽  
Alana Darcher ◽  
Tamara T. Müller ◽  
Jakob H. Macke ◽  
...  
Keyword(s):  
Data Quality ◽  
Single Unit ◽  
Human Subjects ◽  
External Noise ◽  
Hospital Environment ◽  
Noise Sources ◽  
Recording Channels ◽  
Waveform Similarity ◽  
Therapeutic Procedures ◽  
Single Unit Recordings

Single-unit recordings in the brain of behaving human subjects provide a unique opportunity to advance our understanding of neural mechanisms of cognition. These recordings are exclusively performed in medical centers during diagnostic or therapeutic procedures. The presence of medical instruments along with other aspects of the hospital environment limit the control of electrical noise compared to animal laboratory environments. Here, we highlight the problem of an increased occurrence of simultaneous spike events on different recording channels in human single-unit recordings. Most of these simultaneous events were detected in clusters previously labeled as artifacts and showed similar waveforms. These events may result from common external noise sources or from different micro-electrodes recording activity from the same neuron. To address the problem of duplicate recorded events, we introduce an open-source algorithm to identify these artificial spike events based on their synchronicity and waveform similarity. Applying our method to a comprehensive dataset of human single-unit recordings, we demonstrate that our algorithm can substantially increase the data quality of these recordings. Given our findings, we argue that future studies of single-unit activity recorded under noisy conditions should employ algorithms of this kind to improve data quality.

scholarly journals EEG-based diagnostics of the auditory system using cochlear implant electrodes as sensors

2020 ◽  
Author(s):  
Ben Somers ◽  
Christopher J. Long ◽  
Tom Francart
Keyword(s):  
Cochlear Implant ◽  
Auditory Evoked Potentials ◽  
Human Subjects ◽  
Electrode Array ◽  
Brain Regions ◽  
Hearing Impaired ◽  
Brain Responses ◽  
Listening Environments ◽  
Objective Audiometry ◽  
Neural Feedback

AbstractThe cochlear implant is one of the most successful medical prostheses, allowing deaf and severely hearing-impaired persons to hear again by electrically stimulating the auditory nerve. A trained audiologist adjusts the stimulation settings for good speech understanding, known as “fitting” the implant. This process is based on subjective feedback from the user, making it time-consuming and challenging, especially in paediatric or communication-impaired populations. Furthermore, fittings only happen during infrequent sessions at a clinic, and therefore cannot take into account variable factors that affect the user’s hearing, such as physiological changes and different listening environments. Objective audiometry, in which brain responses evoked by auditory stimulation are collected and analysed, removes the need for active patient participation. However, recording of brain responses still requires expensive equipment that is cumbersome to use. An elegant solution is to record the neural signals using the implant itself. We demonstrate for the first time the recording of continuous electroencephalographic (EEG) signals from the implanted intracochlear electrode array in human subjects, using auditory evoked potentials originating from different brain regions. Furthermore, we show that the response morphologies and amplitudes depend crucially on the recording electrode configuration. The integration of an EEG system into cochlear implants paves the way towards chronic neuro-monitoring of hearing-impaired patients in their everyday environment, and neuro-steered hearing prostheses, which can autonomously adjust their output based on neural feedback.

scholarly journals Information limits on neural identification of colored surfaces in natural scenes

2004 ◽  
Vol 21 (3) ◽  
pp. 331-336 ◽  
Author(s):  
DAVID H. FOSTER ◽  
SÉRGIO M.C. NASCIMENTO ◽  
KINJIRO AMANO
Keyword(s):  
Neural Coding ◽  
Neural Representation ◽  
Natural Scenes ◽  
Surface Reflectance ◽  
Surface Color ◽  
Maximum Information ◽  
Information Theoretic ◽  
Urban Scenes ◽  
Rural And Urban ◽  
Information Theoretic Measures

If surfaces in a scene are to be distinguished by their color, their neural representation at some level should ideally vary little with the color of the illumination. Four possible neural codes were considered: von-Kries-scaled cone responses from single points in a scene, spatial ratios of cone responses produced by light reflected from pairs of points, and these quantities obtained with sharpened (opponent-cone) responses. The effectiveness of these codes in identifying surfaces was quantified by information-theoretic measures. Data were drawn from a sample of 25 rural and urban scenes imaged with a hyperspectral camera, which provided estimates of surface reflectance at 10-nm intervals at each of 1344 × 1024 pixels for each scene. In computer simulations, scenes were illuminated separately by daylights of correlated color temperatures 4000 K, 6500 K, and 25,000 K. Points were sampled randomly in each scene and identified according to each of the codes. It was found that the maximum information preserved under illuminant changes varied with the code, but for a particular code it was remarkably stable across the different scenes. The standard deviation over the 25 scenes was, on average, approximately 1 bit, suggesting that the neural coding of surface color can be optimized independent of location for any particular range of illuminants.

Local versus distributed: A poor taxonomy of neural coding strategies

2004 ◽  
Vol 27 (5) ◽  
pp. 700-702
Author(s):  
Michael W. Spratling
Keyword(s):  
Neural Coding ◽  
Neural Representation ◽  
Target Article ◽  
Coding Strategies

Page is to be congratulated for challenging some misconceptions about neural representation. However, his target article, and the commentaries to it, highlight that the terms “local” and “distributed” are open to misinterpretation. These terms provide a poor description of neural coding strategies and a better taxonomy might resolve some of the issues.

scholarly journals Normalized value coding explains dynamic adaptation in the human valuation process

2017 ◽  
Vol 114 (48) ◽  
pp. 12696-12701 ◽  
Author(s):  
Mel W. Khaw ◽  
Paul W. Glimcher ◽  
Kenway Louie
Keyword(s):  
Context Effects ◽  
Human Subjects ◽  
Neural Representation ◽  
Dynamic Adaptation ◽  
Temporal Context ◽  
Decision Variable ◽  
Average Value ◽  
Subjective Value ◽  
Valuation Process ◽  
Value Coding

The notion of subjective value is central to choice theories in ecology, economics, and psychology, serving as an integrated decision variable by which options are compared. Subjective value is often assumed to be an absolute quantity, determined in a static manner by the properties of an individual option. Recent neurobiological studies, however, have shown that neural value coding dynamically adapts to the statistics of the recent reward environment, introducing an intrinsic temporal context dependence into the neural representation of value. Whether valuation exhibits this kind of dynamic adaptation at the behavioral level is unknown. Here, we show that the valuation process in human subjects adapts to the history of previous values, with current valuations varying inversely with the average value of recently observed items. The dynamics of this adaptive valuation are captured by divisive normalization, linking these temporal context effects to spatial context effects in decision making as well as spatial and temporal context effects in perception. These findings suggest that adaptation is a universal feature of neural information processing and offer a unifying explanation for contextual phenomena in fields ranging from visual psychophysics to economic choice.

scholarly journals Neural codes of seeing architectural styles

2016 ◽  
Author(s):  
Heeyoung Choo ◽  
Jack Nasar ◽  
Bardia Nikrahei ◽  
Dirk B. Walther
Keyword(s):  
Architectural Design ◽  
Brain Regions ◽  
Neural Representation ◽  
Neural Encoding ◽  
Substantial Impact ◽  
Fine Grained ◽  
Face Area ◽  
Architectural Styles ◽  
Subordinate Level ◽  
High Level

AbstractImages of iconic buildings, such as the CN Tower, instantly transport us to specific places, such as Toronto. Despite the substantial impact of architectural design on people’s visual experience of built environments, we know little about its neural representation in the human brain. In the present study, we have found patterns of neural activity associated with specific architectural styles in several high-level visual brain regions, but not in primary visual cortex (V1). This finding suggests that the neural correlates of the visual perception of architectural styles stem from style-specific complex visual structure beyond the simple features computed in V1. Surprisingly, the network of brain regions representing architectural styles included the fusiform face area (FFA) in addition to several scene-selective regions. Hierarchical clustering of error patterns further revealed that the FFA participated to a much larger extent in the neural encoding of architectural styles than entry-level scene categories. We conclude that the FFA is involved in fine-grained neural encoding of scenes at a subordinate-level, in our case, architectural styles of buildings. This study for the first time shows how the human visual system encodes visual aspects of architecture, one of the predominant and longest-lasting artefacts of human culture.

scholarly journals Probabilistic representation in human visual cortex reflects uncertainty in serial decisions

2019 ◽  
Author(s):  
R.S. van Bergen ◽  
J.F.M. Jehee
Keyword(s):  
Visual Cortex ◽  
Neural Coding ◽  
Probability Distributions ◽  
Sensory Information ◽  
Population Level ◽  
Neural Representation ◽  
Serial Dependence ◽  
Population Activity ◽  
Human Visual Cortex ◽  
Sensory Uncertainty

AbstractHow does the brain represent the reliability of its sensory evidence? Here, we test whether sensory uncertainty is encoded in cortical population activity as the width of a probability distribution – a hypothesis that lies at the heart of Bayesian models of neural coding. We probe the neural representation of uncertainty by capitalizing on a well-known behavioral bias called serial dependence. Human observers of either sex reported the orientation of stimuli presented in sequence, while activity in visual cortex was measured with fMRI. We decoded probability distributions from population-level activity and found that serial dependence effects in behavior are consistent with a statistically advantageous sensory integration strategy, in which uncertain sensory information is given less weight. More fundamentally, our results suggest that probability distributions decoded from human visual cortex reflect the sensory uncertainty that observers rely on in their decisions, providing critical evidence for Bayesian theories of perception.

Sign in / Sign up

Export Citation Format

Share Document