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 在一生中，人类和其他动物在自然声学环境中学习统计规律。 他们调整听力以强调声音的特征，这对于制作行为很重要 决定。正常听觉的人能够在拥挤的嘈杂场景中感知重要的声音 第一次见面时，请了解个人的讲话。但是，外周听力损失或 中央处理障碍在这些挑战的环境中通常会听到问题，即使声音是 在上面的感知阈值上放大。更好地理解健康和受损的功能 听觉系统将支持这些缺陷的新治疗方法。 该项目将开发计算工具来研究中央听觉处理。软件库将支持 拟合和评估大量编码模型，以描述 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 提出的，但在它们之间进行了相对较少的直接比较。这个项目将使他们的 比较，允许识别对其性能积极贡献的关键特征。这 系统将具有模块化设计，因此可以将来自不同模型的有用元素合并到 具有更大解释力的综合模型。该软件将是开源的，将支持 来自多种记录方式的数据，包括小规模的单个单位电生理和钙 成像数据，以及大规模的本地场和磁性数据。 除了建立有关神经编码的现有假设外，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，数据驱动的模型 事实证明，对于广泛的信号处理问题而言有价值，但它们与现有的价值和关系 神经感觉处理的模型仍有待探索。 感官处理涉及大神经种群的相干活性。在人口进行编码 级别，该系统将支持表征多个神经信号的同时活动的模型和 确定与声音编码有关的人口活动的潜在子空间。感觉编码也受到影响 根据行为环境，反映行为需求的变化和更一般的环境。这 系统将将行为状态变量纳入模型，在该模型中可以调制编码属性 行为环境的变化。