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.

项目概要 在整个生命过程中，人类和其他动物都会在自然声环境中学习统计规律。 他们调整听力以强调声音的特征，这些特征对于做出行为很重要 决定。听力正常的人类能够在拥挤嘈杂的场景中感知重要的声音并 理解人们第一次见面时的讲话。然而，患有周围性听力损失或 中枢处理障碍通常会在这些具有挑战性的环境中出现听力问题，即使声音是 放大到感知阈值之上。更好地了解健康者和受损者的功能 听觉系统将支持针对这些缺陷的新疗法。 该项目将开发计算工具来研究中枢听觉处理。软件库将支持 拟合和评估大量编码模型来描述编码之间的函数关系 随时间变化的自然听觉刺激和相应的神经反应。许多这样的模型已经被 提出了建议，但对它们之间进行的直接比较相对较少。该项目将使他们 比较，可以识别对其性能有积极贡献的关键特征。这 系统将采用模块化设计，以便将不同模型的有用元素组合成 具有更强解释力的综合模型。该软件将是开源的并支持 来自多种记录方式的数据，包括小规模单单元电生理和钙 成像数据以及大规模局部场和脑磁图数据。 除了建立有关神经编码的现有假设之外，该系统还将支持机器学习 使用相同数据集拟合人工神经网络模型的方法。这些大型数据驱动模型 已被证明对广泛的信号处理问题有价值，但它们的价值和与现有的关系 神经感觉处理模型仍有待探索。 感觉处理涉及大量神经群体的连贯活动。研究人群编码 水平上，系统将支持表征多个神经信号同时活动的模型， 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。这 系统将把行为状态变量纳入模型中，其中编码属性可以通过以下方式进行调节 行为背景的变化。