CRCNS: Online optimization for probing high-level auditory representations

CRCNS：用于探测高级听觉表征的在线优化

• 批准号: 10831120
• 负责人: XIAOQIN WANG
• 金额: $ 40.73万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-01 至 2028-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10831120
• 关键词: Animal Vocalization; Area; Artificial Intelligence; Auditory; Auditory area; Brain; Callithrix; Code; Communication; Complex; Computer Models; Computing Methodologies; Diagnosis; Effectiveness; Elements; Explosion; Frequencies; Hearing; Image; Individual; Methods; Modeling; Monkeys; Music; Neurons; Perception; Personal Satisfaction; Population; Postdoctoral Fellow; Primates; Procedures; Process; Research; Research Personnel; Response to stimulus physiology; Sensory; Signal Transduction; Societies; Speech; Stimulus; Structure; System; Training and Education; combinatorial; design; experimental study; graduate student; hearing impairment; hearing loss treatment; improved; neural; neural circuit; neuromechanism; neurophysiology; response; skills; sound; spectral energy; success; therapy development; two-photon; undergraduate student

Biologically important sounds, such as animal vocalization, speech, and tonal music, contain rich harmonics with spectral energy clustered at integer multiples of the fundamental frequency. Although the exact neural coding mechanisms for harmonic sounds remain unclear, recent experiments show that harmonic sensitivity is widespread in the auditory cortices of the marmoset. Since cortical harmonic sensitivity spans multiple octaves, it is derived presumably by combining subcortical inputs that typically prefer only a single frequency. We propose to study harmonic coding in auditory cortex of the marmoset by simultaneous recording of many individual neurons which are probed automatically by an online adaptive stimulus optimization procedure based on explicit computational models of the underlying neural circuits. Conventional methods are incapable of fully characterizing complex harmonic responses because of the combinatorial explosion of the stimulus space, which is a general obstacle for sensory coding research. We propose to overcome this obstacle using an adaptive online approach to harmonic stimulus design. We will apply two broad types of methods, one is to find the optimal stimulus that best drive a neuron, and the other is model-based stimulus design that can effectively identify each given model and compare competing models by finding the stimuli that best distinguish them. We will develop: (1) automated system to characterize harmonic sensitivity of individual neurons across multiple layers of auditory cortex using Neuropixels recording probes, (2) automated system to characterize harmonic sensitivity in auditory cortex across multiple octaves of frequencies using two-photon imaging, and (3) generative circuit models for efficient coding of harmonic sounds in auditory cortex. By restricting the stimuli to harmonic sounds, which are complex enough but still tractable, we believe our methods are more likely to achieve significant success. We have obtained promising preliminary results in several successful online neurophysiological experiments using single-unit recording. Extending our online methods to Neuropixels recording and two-photon imaging is a logical next step and may potentially benefit many researchers working on related problems. We expect to obtain full stimulus-response landscapes of cortical neurons together with inferred circuit models that may explain how exactly a higher-level cortical representation of harmonics may arise from simpler input components, and all these representations will be examined in the context of efficient coding of natural sounds.

生物学上重要的声音，如动物的发声，演讲和音调音乐，包含丰富的谐波， 频谱能量聚集在基频的整数倍处。虽然确切的神经编码 谐波声音的机制仍然不清楚，最近的实验表明，谐波敏感性是 广泛存在于绒猴的听觉皮层中由于皮层谐波敏感度跨越多个倍频程， 它大概是通过组合通常仅偏好单个频率的皮层下输入而导出的。我们 我们建议通过同时记录多个信号来研究绒猴听觉皮层的谐波编码 通过在线自适应刺激优化过程自动探测各个神经元， 对底层神经回路的显式计算模型进行研究。传统方法无法完全 由于刺激空间的组合爆炸而表征复杂的谐波响应， 这是感觉编码研究的一个普遍障碍。我们建议使用一个 自适应在线方法的谐波刺激设计。我们将应用两种广泛类型的方法，一种是找到 最佳刺激最好地驱动神经元，另一种是基于模型的刺激设计，可以有效地 识别每个给定的模型，并通过找到最能区分它们的刺激来比较竞争模型。我们 将开发：（1）自动化系统来表征多个神经元的谐波灵敏度 使用Neuropixels记录探针的听觉皮质层，（2）表征谐波的自动化系统 使用双光子成像在多个倍频程的频率上的听觉皮层的敏感性，以及（3） 在听觉皮层中有效编码谐波声音的生成电路模型。通过将刺激限制在 和声，这是足够复杂，但仍然易于处理，我们相信我们的方法更有可能 取得重大成功。我们已经在几个成功的在线实验中取得了令人鼓舞的初步结果。 神经生理学实验使用单单位记录。将我们的在线方法扩展到Neuropixels 记录和双光子成像是合乎逻辑的下一步，可能会使许多研究人员受益。 相关问题。我们希望获得完整的刺激-反应景观的皮层神经元一起， 推断的电路模型，可以解释如何准确地更高层次的皮层代表谐波可能 从更简单的输入组件中产生，所有这些表示将在高效的背景下进行检查。 自然声音的编码。