CRCNS: Identifying principles of auditory cortical organization with machine learning

CRCNS：通过机器学习识别听觉皮层组织的原理

• 批准号: 10830506
• 负责人: Joseph Gerard Makin
• 金额: $ 35.46万
• 依托单位: UNIVERSITY OF CALIFORNIA AT DAVIS
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-12 至 2028-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10830506
• 关键词: Acoustics; Active Listening; Animals; Auditory; Auditory area; Auditory system; Back; Biological Neural Networks; Brain; Classification; Complex; Data; Electrophysiology (science); Foundations; Frequencies; Hearing Aids; Hearing problem; Human; Image; Length; Location; Macaca; Macaca mulatta; Machine Learning; Measures; Methods; Modeling; Monkeys; Music; Nervous System; Neurons; Performance; Play; Primates; Protocols documentation; Resolution; Saimiri; Sensory; Speech; Stimulus; Structure; System; Testing; Text; Time; Training; Visual; Visual Cortex; Visual Pathways; Voice; area V4; artificial neural network; auditory discrimination; auditory stimulus; biophysical model; deep neural network; design; experimental study; neural network architecture; neurophysiology; novel; preference; response; sound; theories

The human auditory system transforms incoming acoustic information into distinct auditory “objects” that can be interpreted, localized, and integrated with information from the other senses. A human listener can resolve, for example, a monophonic musical recording into piano and guitar, or parse speech audio into a sequence of words. We do not currently understand how this is achieved, nor how transformations in sound processing across cortical regions contribute, especially beyond primary auditory cortex. On the other hand, we now have available another, more easily investigated system that—as of this last decade—solves such problems at human performance levels: the artificial neural network (ANN). Although crude as biophysical models, ANNs strongly resemble biological neural networks in terms of computation and representation. We propose to compare single-unit electrophysiology in macaque auditory cortex with state-of-the-art ANNs trained to solve ecologically relevant auditory tasks. This combination allows us to track how transformations in perceptual representations are distributed and instantiated in the brain; to experiment with multiple ANN architectures and tasks to test hypotheses about why the representations take the forms we observe; and to refine iteratively our stimulus protocols and models. Our objectives are (1) to evaluate these ANNs as encoding models for neurons in macaque auditory cortex, recorded during auditory discrimination tasks; (2) to use the internal structure of ANNs to generate and test novel hypotheses about the topographical and hierarchical organization of non-primary auditory cortex; and (3) to demonstrate “stimulus- based control” of neurons throughout nonprimary auditory cortex: optimizing stimuli via the ANN and then playing to the animals in closed-loop neurophysiology experiments.

人类听觉系统将传入的声学信息转换成不同的听觉“对象”