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Neural mechanisms underlying vocalization perception in realistic listening conditions

现实听力条件下发声感知的神经机制

基本信息

批准号：
10434867
负责人：
Srivatsun Sadagopan
金额：
$ 41.14万
依托单位：
UNIVERSITY OF PITTSBURGH AT PITTSBURGH
依托单位国家：
美国
项目类别：
财政年份：
2018
资助国家：
美国
起止时间：
2018-07-15 至 2024-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10434867
关键词：
Acoustics Address Affect Animal Communication Animal Vocalization Animals Auditory Perception Auditory area Auditory system Behavior monitoring Behavioral Brain Callithrix Categories Cavia Classification Communication Communication impairment Complex Computer Models Data Detection Development Disease Dyslexia Ear Elderly Electrophysiology (science)Ensure Environment Functional disorder Goals Hearing Aids Human Individual Differences Location Modeling Neurons Noise Opsin Pathology Patients Perception Play Population Presbycusis Production Property Role Signal Transduction Speech Speech Perception Stimulus Temporal Lobe Testing Thalamic structure Theoretical model Variant Viral Vector Visual system structure auditory pathway auditory processing autism spectrum disorder awake base experimental study hidden hearing loss in vivo inhibitory neuron insight neural circuit neural model neuromechanism novel novel therapeutic intervention optogenetics predicting response relating to nervous system response sound specific language impairment speech in noise vocalization

项目摘要

ABSTRACT A fundamental question in audition is how complex sounds are accurately recognized despite wide variations in listening conditions. Accurate and robust recognition is especially important for behaviorally critical sounds such as speech or animal vocalizations (calls). Large individual differences in the production of speech or calls, and environmental distortions due to reverberations and noise, result in considerable variability in the acoustics of calls when they arrive at the ear. A critical function of the auditory system is to group these diverse signals into functional categories, such as a call type in animal communication, or words in human speech. In this proposal, we will begin to elucidate the mechanisms by which invariant categorization of calls is accomplished in auditory cortex. In Aim 1, we will first develop and validate a model for how neural circuits might achieve call categorization while remaining invariant to production variability. This model makes specific predictions about the tuning properties of single neurons and neural populations in auditory cortex. We will test these predictions experimentally using in-vivo recordings in awake Guinea pigs. Preliminary data suggests that neurons in superficial cortical layers (L2/3) of primary auditory cortex (A1) encode ‘mid-level’ features that underlie production-invariant call categorization. In Aim 2, we will test the hypothesis that both production-invariant selectivity to specific call types, as well as a high degree of invariance to listening conditions co-emerge in A1 L2/3. We will analyze the responses of single neurons as well as neural populations in thalamus, A1 layer 4 (L4), and A1 L2/3 to call stimuli presented in ideal and systematically degraded listening conditions. Preliminary data suggest that A1 L2/3 neurons, but not thalamic neurons, are able to maintain their feature selectivity in a wide range of listening conditions. Finally, in Aim 3, we will test the hypothesis that cortical inhibition is crucial for generating invariance to listening conditions, but not call selectivity. Specifically, we will use optogenetic approaches with novel viral vectors to characterize the effects of decreased inhibition on selectivity and invariance in A1 single neurons and the A1 population. Together, the theoretical model, and behavioral, electrophysiological, and optogenetic data will give rise to a general computational principle by which the auditory cortex extracts useful features from noisy inputs. Insights gained from these experiments will provide a novel framework for understanding auditory processing in normal circuits, as well as during circuit dysfunction in communication disorders such as autism, dyslexia and specific language impairment.

摘要听觉中的一个基本问题是如何准确地识别复杂的声音，尽管声音的变化很大。听的条件。准确和鲁棒的识别对于行为关键的声音尤其重要例如语音或动物发声（呼叫）。在言语或叫声的产生上存在很大的个体差异，以及由于混响和噪声引起的环境失真，导致声学的相当大的变化当它们到达耳朵的时候。听觉系统的一个关键功能是将这些不同的信号分组分为功能类别，例如动物交流中的呼叫类型，或人类语言中的单词。在这建议，我们将开始阐明的机制，不变的分类电话是完成在听觉皮层。在目标1中，我们将首先开发和验证神经回路如何实现呼叫的模型。分类，同时保持不变的生产变化。该模型具体预测了听觉皮层单个神经元和神经元群的调谐特性。我们将检验这些预测在清醒的豚鼠体内进行实验记录。初步数据表明，初级听觉皮层（A1）的表层皮层（L2/3）编码“中级”特征，生产不变呼叫分类。在目标2中，我们将测试假设，即生产不变对特定呼叫类型的选择性以及对监听条件的高度不变性共同出现在A1中 L2/3。我们将分析单个神经元以及丘脑A1层4的神经群体的反应 (L4)和A1 L2/3来调用在理想的和系统退化的听条件下呈现的刺激。初步数据表明，A1 L2/3神经元，但不是丘脑神经元，能够保持其功能的选择性，广泛的听力条件。最后，在目标3中，我们将检验皮质抑制至关重要的假设用于产生对监听条件的不变性，而不是呼叫选择性。具体来说，我们将使用光遗传学使用新型病毒载体表征抑制降低对选择性的影响的方法 A1单神经元和A1群体的不变性。理论模型和行为模型，电生理学和光遗传学数据将产生一个通用的计算原理，听觉皮层从噪声输入中提取有用的特征。从这些实验中获得的见解将提供一个一个新的框架，了解听觉处理在正常电路，以及在电路功能障碍在沟通障碍，如自闭症，诵读困难和特定的语言障碍。

项目成果

期刊论文数量（8）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Pupillometry as a reliable metric of auditory detection and discrimination across diverse stimulus paradigms in animal models.

在动物模型中，各种刺激范式跨不同刺激范式的听觉检测和歧视的可靠度量。

DOI：
10.1038/s41598-021-82340-y
发表时间：
2021-02-04
期刊：
Scientific reports
影响因子：
4.6
作者：
Montes-Lourido P;Kar M;Kumbam I;Sadagopan S
通讯作者：
Sadagopan S

Vocalization categorization behavior explained by a feature-based auditory categorization model.

DOI：
10.7554/elife.78278
发表时间：
2022-10-13
期刊：
eLife
影响因子：
7.7
作者：
Kar M;Pernia M;Williams K;Parida S;Schneider NA;McAndrew M;Kumbam I;Sadagopan S
通讯作者：
Sadagopan S

Neuronal selectivity to complex vocalization features emerges in the superficial layers of primary auditory cortex.

DOI：
10.1371/journal.pbio.3001299
发表时间：
2021-06
期刊：
PLoS biology
影响因子：
9.8
作者：
Montes-Lourido P;Kar M;David SV;Sadagopan S
通讯作者：
Sadagopan S

Updates to the guinea pig animal model for in-vivo auditory neuroscience in the low-frequency hearing range.

在低频听力范围内，在体内听觉神经科学的豚鼠动物模型中更新。

DOI：
10.1016/j.heares.2022.108603
发表时间：
2022-10
期刊：
HEARING RESEARCH
影响因子：
2.8
作者：
Montes-Lourido, Pilar;Kar, Manaswini;Pernia, Marianny;Parida, Satyabrata;Sadagopan, Srivatsun
通讯作者：
Sadagopan, Srivatsun

Adaptive mechanisms facilitate robust performance in noise and in reverberation in an auditory categorization model.