Mechanisms of memory retrieval in slow-wave sleep : memory retrieval in slow-wave sleep

Study Objectives: Memories are strengthened during sleep. The benefits of sleep for memory can be enhanced by re-exposing the sleeping brain to auditory cues; a technique known as targeted memory reactivation (TMR). Prior studies have not assessed the nature of the retrieval mechanisms underpinning TMR: the matching process between auditory stimuli encountered during sleep and previously encoded memories. We carried out two experiments to address this issue. Methods: In Experiment 1, participants associated words with verbal and non-verbal auditory stimuli before an overnight interval in which subsets of these stimuli were replayed in slow-wave sleep. We repeated this paradigm in Experiment 2 with the single difference that the gender of the verbal auditory stimuli was switched between learning and sleep. Results: In Experiment 1, forgetting of cued (vs. non-cued) associations was reduced by TMR with verbal and non-verbal cues to similar extents. In Experiment 2, TMR with identical non-verbal cues reduced forgetting of cued (vs. non-cued) associations, replicating Experiment 1. However, TMR with non-identical verbal cues reduced forgetting of both cued and non-cued associations. Conclusions: These experiments suggest that the memory effects of TMR are influenced by the acoustic overlap between stimuli delivered at training and sleep. Our findings hint at the existence of two processing routes for memory retrieval during sleep. Whereas TMR with acoustically identical cues may reactivate individual associations via simple episodic matching, TMR with non-identical verbal cues may utilise linguistic decoding mechanisms, resulting in widespread reactivation across a broad category of memories

ARSTREAM: A Neural Network Model of Auditory Scene Analysis and Source Segregation

Multiple sound sources often contain harmonics that overlap and may be degraded by environmental noise. The auditory system is capable of teasing apart these sources into distinct mental objects, or streams. Such an "auditory scene analysis" enables the brain to solve the cocktail party problem. A neural network model of auditory scene analysis, called the AIRSTREAM model, is presented to propose how the brain accomplishes this feat. The model clarifies how the frequency components that correspond to a give acoustic source may be coherently grouped together into distinct streams based on pitch and spatial cues. The model also clarifies how multiple streams may be distinguishes and seperated by the brain. Streams are formed as spectral-pitch resonances that emerge through feedback interactions between frequency-specific spectral representaion of a sound source and its pitch. First, the model transforms a sound into a spatial pattern of frequency-specific activation across a spectral stream layer. The sound has multiple parallel representations at this layer. A sound's spectral representation activates a bottom-up filter that is sensitive to harmonics of the sound's pitch. The filter activates a pitch category which, in turn, activate a top-down expectation that allows one voice or instrument to be tracked through a noisy multiple source environment. Spectral components are suppressed if they do not match harmonics of the top-down expectation that is read-out by the selected pitch, thereby allowing another stream to capture these components, as in the "old-plus-new-heuristic" of Bregman. Multiple simultaneously occuring spectral-pitch resonances can hereby emerge. These resonance and matching mechanisms are specialized versions of Adaptive Resonance Theory, or ART, which clarifies how pitch representations can self-organize durin learning of harmonic bottom-up filters and top-down expectations. The model also clarifies how spatial location cues can help to disambiguate two sources with similar spectral cures. Data are simulated from psychophysical grouping experiments, such as how a tone sweeping upwards in frequency creates a bounce percept by grouping with a downward sweeping tone due to proximity in frequency, even if noise replaces the tones at their interection point. Illusory auditory percepts are also simulated, such as the auditory continuity illusion of a tone continuing through a noise burst even if the tone is not present during the noise, and the scale illusion of Deutsch whereby downward and upward scales presented alternately to the two ears are regrouped based on frequency proximity, leading to a bounce percept. Since related sorts of resonances have been used to quantitatively simulate psychophysical data about speech perception, the model strengthens the hypothesis the ART-like mechanisms are used at multiple levels of the auditory system. Proposals for developing the model to explain more complex streaming data are also provided.Air Force Office of Scientific Research (F49620-01-1-0397, F49620-92-J-0225); Office of Naval Research (N00014-01-1-0624); Advanced Research Projects Agency (N00014-92-J-4015); British Petroleum (89A-1204); National Science Foundation (IRI-90-00530); American Society of Engineering Educatio

ARSTREAM: A Neural Network Model of Auditory Scene Analysis and Source Segregation

Multiple sound sources often contain harmonics that overlap and may be degraded by environmental noise. The auditory system is capable of teasing apart these sources into distinct mental objects, or streams. Such an "auditory scene analysis" enables the brain to solve the cocktail party problem. A neural network model of auditory scene analysis, called the AIRSTREAM model, is presented to propose how the brain accomplishes this feat. The model clarifies how the frequency components that correspond to a give acoustic source may be coherently grouped together into distinct streams based on pitch and spatial cues. The model also clarifies how multiple streams may be distinguishes and seperated by the brain. Streams are formed as spectral-pitch resonances that emerge through feedback interactions between frequency-specific spectral representaion of a sound source and its pitch. First, the model transforms a sound into a spatial pattern of frequency-specific activation across a spectral stream layer. The sound has multiple parallel representations at this layer. A sound's spectral representation activates a bottom-up filter that is sensitive to harmonics of the sound's pitch. The filter activates a pitch category which, in turn, activate a top-down expectation that allows one voice or instrument to be tracked through a noisy multiple source environment. Spectral components are suppressed if they do not match harmonics of the top-down expectation that is read-out by the selected pitch, thereby allowing another stream to capture these components, as in the "old-plus-new-heuristic" of Bregman. Multiple simultaneously occuring spectral-pitch resonances can hereby emerge. These resonance and matching mechanisms are specialized versions of Adaptive Resonance Theory, or ART, which clarifies how pitch representations can self-organize durin learning of harmonic bottom-up filters and top-down expectations. The model also clarifies how spatial location cues can help to disambiguate two sources with similar spectral cures. Data are simulated from psychophysical grouping experiments, such as how a tone sweeping upwards in frequency creates a bounce percept by grouping with a downward sweeping tone due to proximity in frequency, even if noise replaces the tones at their interection point. Illusory auditory percepts are also simulated, such as the auditory continuity illusion of a tone continuing through a noise burst even if the tone is not present during the noise, and the scale illusion of Deutsch whereby downward and upward scales presented alternately to the two ears are regrouped based on frequency proximity, leading to a bounce percept. Since related sorts of resonances have been used to quantitatively simulate psychophysical data about speech perception, the model strengthens the hypothesis the ART-like mechanisms are used at multiple levels of the auditory system. Proposals for developing the model to explain more complex streaming data are also provided.Air Force Office of Scientific Research (F49620-01-1-0397, F49620-92-J-0225); Office of Naval Research (N00014-01-1-0624); Advanced Research Projects Agency (N00014-92-J-4015); British Petroleum (89A-1204); National Science Foundation (IRI-90-00530); American Society of Engineering Educatio

Contributions of local speech encoding and functional connectivity to audio-visual speech perception

Seeing a speaker’s face enhances speech intelligibility in adverse environments. We investigated the underlying network mechanisms by quantifying local speech representations and directed connectivity in MEG data obtained while human participants listened to speech of varying acoustic SNR and visual context. During high acoustic SNR speech encoding by temporally entrained brain activity was strong in temporal and inferior frontal cortex, while during low SNR strong entrainment emerged in premotor and superior frontal cortex. These changes in local encoding were accompanied by changes in directed connectivity along the ventral stream and the auditory-premotor axis. Importantly, the behavioral benefit arising from seeing the speaker’s face was not predicted by changes in local encoding but rather by enhanced functional connectivity between temporal and inferior frontal cortex. Our results demonstrate a role of auditory-frontal interactions in visual speech representations and suggest that functional connectivity along the ventral pathway facilitates speech comprehension in multisensory environments

Idealized computational models for auditory receptive fields

This paper presents a theory by which idealized models of auditory receptive fields can be derived in a principled axiomatic manner, from a set of structural properties to enable invariance of receptive field responses under natural sound transformations and ensure internal consistency between spectro-temporal receptive fields at different temporal and spectral scales. For defining a time-frequency transformation of a purely temporal sound signal, it is shown that the framework allows for a new way of deriving the Gabor and Gammatone filters as well as a novel family of generalized Gammatone filters, with additional degrees of freedom to obtain different trade-offs between the spectral selectivity and the temporal delay of time-causal temporal window functions. When applied to the definition of a second-layer of receptive fields from a spectrogram, it is shown that the framework leads to two canonical families of spectro-temporal receptive fields, in terms of spectro-temporal derivatives of either spectro-temporal Gaussian kernels for non-causal time or the combination of a time-causal generalized Gammatone filter over the temporal domain and a Gaussian filter over the logspectral domain. For each filter family, the spectro-temporal receptive fields can be either separable over the time-frequency domain or be adapted to local glissando transformations that represent variations in logarithmic frequencies over time. Within each domain of either non-causal or time-causal time, these receptive field families are derived by uniqueness from the assumptions. It is demonstrated how the presented framework allows for computation of basic auditory features for audio processing and that it leads to predictions about auditory receptive fields with good qualitative similarity to biological receptive fields measured in the inferior colliculus (ICC) and primary auditory cortex (A1) of mammals.Comment: 55 pages, 22 figures, 3 table

Selective Attention and Audiovisual Integration: Is Attending to Both Modalities a Prerequisite for Early Integration?

Interactions between multisensory integration and attention were studied using a combined audiovisual streaming design and a rapid serial visual presentation paradigm. Event-related potentials (ERPs) following audiovisual objects (AV) were compared with the sum of the ERPs following auditory (A) and visual objects (V). Integration processes were expressed as the difference between these AV and (A + V) responses and were studied while attention was directed to one or both modalities or directed elsewhere. Results show that multisensory integration effects depend on the multisensory objects being fully attended—that is, when both the visual and auditory senses were attended. In this condition, a superadditive audiovisual integration effect was observed on the P50 component. When unattended, this effect was reversed; the P50 components of multisensory ERPs were smaller than the unisensory sum. Additionally, we found an enhanced late frontal negativity when subjects attended the visual component of a multisensory object. This effect, bearing a strong resemblance to the auditory processing negativity, appeared to reflect late attention-related processing that had spread to encompass the auditory component of the multisensory object. In conclusion, our results shed new light on how the brain processes multisensory auditory and visual information, including how attention modulates multisensory integration processes

A Visionary Approach to Listening: Determining The Role Of Vision In Auditory Scene Analysis

To recognize and understand the auditory environment, the listener must first separate sounds that arise from different sources and capture each event. This process is known as auditory scene analysis. The aim of this thesis is to investigate whether and how visual information can influence auditory scene analysis. The thesis consists of four chapters. Firstly, I reviewed the literature to give a clear framework about the impact of visual information on the analysis of complex acoustic environments. In chapter II, I examined psychophysically whether temporal coherence between auditory and visual stimuli was sufficient to promote auditory stream segregation in a mixture. I have found that listeners were better able to report brief deviants in an amplitude modulated target stream when a visual stimulus changed in size in a temporally coherent manner than when the visual stream was coherent with the non-target auditory stream. This work demonstrates that temporal coherence between auditory and visual features can influence the way people analyse an auditory scene. In chapter III, the integration of auditory and visual features in auditory cortex was examined by recording neuronal responses in awake and anaesthetised ferret auditory cortex in response to the modified stimuli used in Chapter II. I demonstrated that temporal coherence between auditory and visual stimuli enhances the neural representation of a sound and influences which sound a neuron represents in a sound mixture. Visual stimuli elicited reliable changes in the phase of the local field potential which provides mechanistic insight into this finding. Together these findings provide evidence that early cross modal integration underlies the behavioural effects in chapter II. Finally, in chapter IV, I investigated whether training can influence the ability of listeners to utilize visual cues for auditory stream analysis and showed that this ability improved by training listeners to detect auditory-visual temporal coherence