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 聆听噪音是日常听力的核心问题。 无关紧要的干扰物，就像您在繁华的咖啡店里与某人交谈时。 因此，识别出听觉神经中的尖峰模式。 兴趣，听觉系统必须分开或抑制典型的效果。 人类的听力是惊人的噪音，但听众的听力损失或其他形式的听众 在嘈杂的环境中，听力斗争受损 - 当代助听器的帮助并没有太大帮助。 以前在噪声稳健性的神经基础上的工作已经采用了简单的合成噪声源， 缺乏现实世界中存在的结构，这项工作集中于皮层区域或 主要的听觉皮层。 申请人的博士工作，他考虑了评估噪音的来源，并利用了大规模的噪音。 FMRI提供的覆盖范围可检查整个人类听觉皮层的噪声 “背景噪声”被用作具有统计特性的自然声音，随着时间的推移而稳定的稳定性 （即是静止的），传达了几乎没有关于世界的新信息（例如，沼泽昆虫，空调， 人行道上的降雨）。 静静的声音以及嵌入在现实世界中的噪音中 背景会大大改变响应，但非主要响应大大改变了 强大 表明对现实世界的背景噪音变得强大需要不同的机制。 申请人的论文工作证明了噪声不变性的位置，但是了解如何需要 具有更精细的空间和时间分辨率的数据，而支撑的博士后研究将包括 使用Marmosset的单位电学培训。 单位噪声稳健性在人工条件下，将这种工作扩展到现实世界的噪声。 探测现实世界背景的哪些方面破坏了前景的编码。 探测器的线性重新构造技术代表AIM 2B。 噪声不变性任务的网络，并将其用作编码模型来预测单单元响应。 此外，此类网络将部署为非线性解码算法，重建刺激 神经元的人群。 在副干预中，在副哪层中被理解的区域。 助听器算法或神经假体的改进。 申请人的长期目标是为听力损失开发Marmoset模型。