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，例如动物发声、言语和音调音乐，包含丰富的和声 频谱能量聚集在基频的整数倍处。尽管精确的神经编码 谐波声音的机制仍不清楚，最近的实验表明谐波敏感性 广泛存在于狨猴的听觉皮层中。由于皮质谐波敏感性跨越多个八度音阶， 它大概是通过组合通常只喜欢单一频率的皮层下输入而得出的。我们 提议通过同时记录许多声音来研究狨猴听觉皮层的谐波编码 通过基于在线自适应刺激优化程序自动探测的单个神经元 底层神经回路的显式计算模型。传统方法无法完全 表征由于刺激空间的组合爆炸而产生的复杂谐波响应， 这是感觉编码研究的普遍障碍。我们建议使用以下方法来克服这一障碍 谐波刺激设计的自适应在线方法。我们将应用两大类方法，一是寻找 最好的刺激是最能驱动神经元的刺激，另一个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）自动化系统来表征跨多个单个神经元的谐波敏感性 使用 Neuropixels 记录探针的听觉皮层层，(2) 自动化系统来表征谐波 使用双光子成像在多个八度频率范围内听觉皮层的灵敏度，以及 (3) 用于有效编码听觉皮层谐波的生成电路模型。通过限制刺激 和声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。我们在多个成功的在线项目中取得了有希望的初步结果 使用单单元记录的神经生理学实验。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，可能使许多研究人员受益 关于相关问题。我们期望获得皮质神经元的完整刺激响应景观 推断电路模型可以解释谐波的高级皮质表示如何准确地 来自更简单的输入组件，所有这些表示都将在有效的背景下进行检查 自然声音的编码。