Mechanisms for invariance in auditory cortex: Investigations with marmoset electrophysiology

听觉皮层不变性的机制：狨猴电生理学研究

• 批准号: 10115690
• 负责人: Alexander James Eaton Kell
• 金额: $ 2.38万
• 依托单位: COLUMBIA UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-03-19 至 2021-07-16
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10115690
• 关键词: Acoustic Nerve; Address; Air; Algorithms; Area; Attention; Auditory; Auditory Prosthesis; Auditory area; Auditory system; Basic Science; Brain; Callithrix; Clinical; Code; Coffee; Computer Models; Data; Data Analyses; Development; Electrophysiology (science); Environment; Functional Magnetic Resonance Imaging; Goals; Grain; Hearing; Hearing Aids; Human; Impaired cognition; Individual; Insecta; Investigation; Measures; Microelectrodes; Modeling; Neurons; Noise; Pattern; Performance; Physiological; Population; Presbycusis; Primates; Process; Property; Quality of life; Rain; Research; Silicon; Source; Stimulus; Structure; Techniques; Texture; Time; Training; Work; algorithm training; base; deep learning; deep neural network; experience; experimental study; hearing impairment; improved; interest; neural circuit; neural prosthesis; neuromechanism; programs; reconstruction; relating to nervous system; response; signal processing; social; sound; temporal measurement

Project Summary Listening in noise is a core problem in everyday hearing. Sound sources of interest routinely occur amid irrelevant distractors, as when you talk with someone in a bustling coffee shop. This background “noise” distorts the pattern of spikes in the auditory nerve, often to a profound degree. Thus, to recognize sources of interest, the auditory system must somehow separate or suppress the effects of the background. Typical human hearing is remarkably noise-robust, but listeners with age-related hearing loss or other forms of impaired hearing struggle in noisy environments – and are not much helped by contemporary hearing aids. Previous work on the neural basis of noise robustness has typically employed simple, synthetic noise sources, which lack the structure present in real-world sounds, and this work has focused on subcortical regions or on primary auditory cortex. Reasoning that real-world conditions might necessitate more complicated solutions, in the applicant's doctoral work, he considered everyday sources of noise, and leveraged the large-scale coverage afforded by fMRI to examine noise robustness throughout human auditory cortex. Real-world “background noise” was operationalized as a natural sound with statistical properties that are stable over time (i.e., are stationary), conveying little new information about the world (e.g., swamp insects, an air conditioner, rain on pavement). The applicant measured fMRI responses in human listeners to a broad set of natural sounds presented in quiet, as well as embedded in the real-world background noises. Primary auditory cortical responses were substantially altered by the background, but non-primary responses were substantially more robust. This effect was not seen for simple synthetic backgrounds as had been used in previous work, suggesting that becoming robust to real-world background noises require different mechanisms. The applicant's thesis work demonstrates where noise invariance arises, but understanding how will require data with finer spatial and temporal resolution, and thus the proposed postdoctoral research will consist of training in single-unit electrophysiology using marmosets. Aim 1A builds on previous work examining single- unit noise robustness in artificial conditions, extending such work to real-world noise. Aim 1B leverages texture models to probe what aspects of real-world backgrounds disrupt the encoding of foregrounds. Aim 2A deploys linear reconstruction techniques to probe population representations. Aim 2B involves optimizing deep neural networks for noise invariance tasks, and using them as an encoding model to predict single-unit responses. Furthermore, such networks will be deployed as nonlinear decoding algorithms, reconstructing stimuli from neuronal populations. Throughout all aims, the work will characterize neuronal responses in non-primary areas, and in particular in parabelt, which is understudied in primates. The proposed work may enable improvements in hearing aid algorithms or neural prosthetics. Lastly, this training will lay the groundwork for the applicant's long-term goal of developing a marmoset model for hearing loss.

项目摘要 在噪音中听是日常听力中的一个核心问题。感兴趣的声音来源通常出现在 无关紧要的干扰，就像你在熙熙攘攘的咖啡店里与某人交谈时一样。这个背景“噪音” 会扭曲听神经中的棘波模式，通常会严重扭曲。因此，要认识到 感兴趣，听觉系统必须以某种方式分离或抑制背景的影响。典型 人类的听力对噪音非常健壮，但与年龄相关的听力损失或其他形式的听力损失的听众 听力受损的人在嘈杂的环境中挣扎--现代助听器对他们帮助不大。 先前关于噪声稳健性的神经基础的工作通常使用简单的合成噪声源， 它们缺乏真实世界声音中存在的结构，这项工作主要集中在皮质下区域或 初级听觉皮质。推理现实世界的条件可能需要更复杂的解决方案，在 申请者的博士工作，他考虑了日常噪音的来源，并利用大规模 功能磁共振成像提供的覆盖范围，以检查整个人类听觉皮质的噪声稳健性。现实世界 背景噪声是一种具有随时间稳定的统计特性的自然声音 (即固定的)，传达的关于世界的新信息很少(例如，沼泽昆虫，空调， 人行道上有雨)。申请人测量了人类听众对一组广泛的自然语言的fMRI反应 声音呈现在安静中，也嵌入在真实世界的背景噪音中。初级听觉皮质 回答被背景改变了很多，但非主要的反应要多得多 很健壮。这种效果在简单的合成背景中看不到，就像以前的工作中使用的那样， 这表明，要对真实世界的背景噪音保持稳健，需要不同的机制。 申请者的论文工作展示了噪声不变性出现的地方，但理解如何 更精细的空间和时间分辨率的数据，因此拟议的博士后研究将包括 使用绒毛进行单单位电生理学培训。目标1A建立在之前审查单一- 单位噪声在人工条件下的稳健性，将这种工作扩展到真实世界的噪声。AIM 1B利用纹理 模型来探索现实世界背景的哪些方面扰乱了前景的编码。AIM 2A部署 用于探测总体表示的线性重建技术。目标2B涉及优化深层神经 网络用于噪声不变性任务，并将其用作预测单个单位响应的编码模型。 此外，这种网络将被部署为非线性解码算法，从 神经元群。在所有的目标中，这项工作将描述非初级神经元的反应。 地区，特别是在帕拉贝尔特，这是在灵长类动物的研究不足。拟议的工作可能会使 助听器算法或神经假体的改进。最后，这次培训将为 申请人的长期目标是开发一种用于听力损失的绒猴模型。