Dissemination of tools and methods for modeling state-dependent neural sensory coding

传播状态依赖神经感觉编码建模工具和方法

• 批准号: 10693569
• 负责人: Stephen V David
• 金额: $ 8.04万
• 依托单位: OREGON HEALTH & SCIENCE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-05-01 至 2023-09-15
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10693569
• 关键词: Acoustics; Animals; Auditory Perceptual Disorders; Auditory system; Behavioral; Brain; Calcium; Code; Complex; Computer software; Crowding; Data; Data Set; Devices; Electrophysiology (science); Elements; Environment; Hearing; Hearing problem; Human; Image; Impairment; Individual; Judgment; Libraries; Life; Magnetoencephalography; Methods; Modality; Modeling; Neural Network Simulation; Patients; Performance; Peripheral; Population; Problem Solving; Property; Sensory; Speech; System; Time; artificial neural network; auditory processing; auditory stimulus; behavior influence; computerized tools; data-driven model; design; experimental study; hearing impairment; insight; machine learning method; nervous system disorder; neural; neural model; neurotransmission; normal hearing; novel; open source; response; signal processing; sound; statistical learning; tool

Project Summary Throughout life, humans and other animals learn statistical regularities in the natural acoustic environment. They adapt their hearing to emphasize the features of sound that are important for making behavioral decisions. Normal-hearing humans are able to perceive important sounds in crowded noisy scenes and to understand the speech of individuals the first time they meet. However, patients with peripheral hearing loss or central processing disorders often have problems hearing in these challenging settings, even when sound is amplified above perceptual threshold. A better understanding of the function of the healthy and impaired auditory system will support new treatments for these deficits. This project will develop computational tools to study central auditory processing. A software library will support fitting and evaluating a large number of encoding models to describe the functional relationship between a time-varying natural auditory stimulus and the corresponding neural response. Many such models have been proposed, but relatively few direct comparisons have been made between them. This project will enable their comparison, allowing identification of the key features that contribute positively to their performance. The system will have a modular design so that useful elements from different models can be combined into comprehensive models with even greater explanatory power. The software will be open source and will support data from multiple recording modalities, including small-scale single unit electrophysiological and calcium imaging data, as well as large-scale local field and magnetoencephalography data. In addition to building on existing hypotheses about neural coding, the system will support machine learning methods for fitting artificial neural network models using the same datasets. These large, data-driven models have proven valuable for wide ranging signal processing problems, but their value and relation to existing models for neural sensory processing remain to be explored. Sensory processing involves coherent activity of large neural populations. To study coding at the population level, the system will support models that characterize the simultaneous activity of multiple neural signals and identifies latent subspaces of population activity related to sound encoding. Sensory coding is also influenced by behavioral context, reflecting changes in behavioral demands and the more general environment. The system will incorporate behavioral state variables into models, where encoding properties can be modulated by changes in behavioral context.

项目摘要 在整个生命过程中，人类和其他动物在自然声学环境中学习统计学。 他们调整自己的听觉，以强调声音的特征，这些特征对行为的产生很重要。 决策听力正常的人能够在拥挤嘈杂的场景中感知重要的声音， 第一次见面就能听懂对方的话。但是，周围性听力损失或 中央处理障碍通常在这些具有挑战性的环境中有听力问题，即使声音 被放大到感知阈值以上更好地了解健康和受损的功能 听觉系统将为这些缺陷提供新的治疗方法。 本计画将发展计算工具以研究中央听觉处理。软件库将支持 拟合和评估大量编码模型来描述 时变自然听觉刺激和相应的神经反应。许多这样的模型已经被 提出，但相对较少的直接比较，他们之间。该项目将使其 通过比较，可以确定对其性能有积极贡献的关键特征。的 系统将采用模块化设计，以便将不同型号的有用元素组合成 具有更强解释力的综合模型。该软件将是开源的，并将支持 来自多种记录方式的数据，包括小规模单单位电生理和钙 成像数据以及大规模局部场和脑磁图数据。 除了建立在现有的关于神经编码的假设之上，该系统还将支持机器学习 使用相同的数据集拟合人工神经网络模型的方法。这些大型的数据驱动模型 已经被证明对广泛的信号处理问题是有价值的，但是它们的价值和与现有的 神经感觉处理的模型仍有待探索。 感觉处理涉及大量神经群体的连贯活动。在人群中学习编码 该系统将支持表征多个神经信号的同时活动的模型， 识别与声音编码相关的群体活动的潜在子空间。感觉编码也受到影响 通过行为背景，反映行为需求和更一般环境的变化。的 系统将把行为状态变量合并到模型中，其中编码属性可以通过 行为环境的变化。