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音信息转换为不同的听觉“对象”， 可以被解释、本地化，并与人类听众的其他感官信息相结合。 例如，将单音音乐录音解析为钢琴和吉他，或者将语音音频解析为 我们目前不了解这是如何实现的，也不了解如何进行转换。 跨皮质区域的声音处理有所贡献，尤其是初级听觉皮层之外的声音处理。 另一方面，我们现在有了另一个更容易研究的系统——截至最后一次 十年——在人类表现水平上解决此类问题：人工神经网络（ANN）。 尽管作为生物物理模型很粗糙，但人工神经网络在以下方面与生物神经网络非常相似： 我们建议比较猕猴的单单位电生理学。 听觉皮层配备最先进的人工神经网络，经过训练可以解决生态相关的听觉任务。 组合使我们能够跟踪感知表征的转换如何分布和 在大脑中实例化；尝试多种 ANN 架构和任务来测试有关的假设 为什么表征采用我们观察到的形式；并迭代地完善我们的刺激方案和 我们的目标是 (1) 评估这些 ANN 作为猕猴神经元的编码模型。 (2)利用ANN的内部结构 生成并测试有关非初级组织的拓扑和层级组织的新假设 听觉皮层；（3）展示整个非初级神经元的“基于刺激的控制” 听觉皮层：通过人工神经网络优化刺激，然后以闭环方式给动物玩耍 神经生理学实验。