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Nonlinear dynamics of complex sound processing in auditory cortex

听觉皮层复杂声音处理的非线性动力学

基本信息

批准号：
9027828
负责人：
Brian J. Malone
金额：
$ 36.99万
依托单位：
UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
依托单位国家：
美国
项目类别：
财政年份：
2012
资助国家：
美国
起止时间：
2012-04-01 至 2018-03-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9027828
关键词：
Acoustics Algorithms Animals Attention Auditory Auditory area Auditory system Behavior Control Behavior Therapy Benchmarking Binding Characteristics Clinical Code Communication Communication impairment Complex Comprehension Cues Data Dependence Detection Development Devices Ensure Environment Exhibits Frequencies Goals Hearing Hearing Aids Hearing problem Human Impairment Knowledge Language Development Linear Models Methods Modeling Monkeys Neurons Noise Non-linear Models Nonlinear Dynamics Pathology Patients Pattern Perception Performance Peripheral Phase Physiological Population Process Property Prosthesis Protocols documentation Psychometrics Reading Recording of previous events Research Saimiri Sampling Self-Help Devices Services Shapes Signal Transduction Speech Speech Intelligibility Stimulus System Techniques Testing Training awake base defined contribution design hearing impairment improved insight interest neural prosthesis neuromechanism novel novel therapeutic intervention predicting response preference receptive field relating to nervous system research study response sound vocalization

项目摘要

DESCRIPTION (provided by applicant): The proposed experiments define how cortical neurons support robust perception of complex sounds, such as speech and other communication sounds, in natural listening environments that include background noise. Signal in noise (SIN) processing has primarily been studied psychophysically; the neural mechanisms that support the remarkable tolerance to noise exhibited by normal hearing are not well understood. We focus on the encoding of low frequency envelope information because it is crucial for intelligible speech and improving speech intelligibility for the hearing impaired is an important clinical goal. More broadly, dynamic features of sound envelopes, such as common onsets, offsets, and modulation characteristics, drive auditory scene segmentation. These features are also particularly well represented in the response dynamics of cortical neurons. However, it has proven difficult to develop a general framework for understanding cortical envelope processing because the relationship between the stimulus envelope and the neural response pattern is typically both complex and substantially nonlinear. We hypothesize that the nonlinear dynamics of cortical responses endow them with a temporal precision that is essential to the robustness of SIN processing. To test this hypothesis, we will employ a novel nonlinear modeling framework to estimate spectrotemporal receptive fields (STRFs) of neurons recorded from the core auditory fields of awake behaving squirrel monkeys using 16- channel linear probes. We will evaluate the ability of nonlinear STRF models - including reduced (e.g., linear) and modified forms - to describe the dynamics of cortical responses to sounds with simple, parametrically varied envelopes (Aim 1). We will compare the performances of the models against real neurons in encoding complex vocalizations embedded in noise (Aim 2), and test candidate neural mechanisms for 'denoising' those signals in the context of optimal Bayesian population decoding methods. Finally, we will assess the effect of attentional filtering on SIN processing by recording from animals presented with identical complex stimuli while engaged in separate tasks, only one of which requires attention to detailed envelope features (i.e., modulation frequency change detection versus sound offset detection), while simultaneously deriving STRF models for subsequent comparison (Aim 3). These experiments will provide valuable insight into candidate neural mechanisms that support both bottom-up and top-down aspects of auditory scene segmentation, and support rigorous quantitative model-based approaches to characterizing laminar transformations in the cortical representation of complex sounds.

描述（由申请人提供）：拟议的实验定义了皮层神经元如何在包括背景噪音的自然听力环境中支持对复杂声音（如语音和其他交流声音）的鲁棒感知。噪声中信号（SIN）处理主要是从心理物理角度进行研究的；支持正常听力表现出的对噪音的显著耐受性的神经机制尚不清楚。低频包络信息的编码对语音的可理解性至关重要，提高听力障碍患者的语音可理解性是一项重要的研究课题