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.

项目总结