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，都包含丰富的谐波与 光谱能聚集在基本频率的整数倍数处。虽然确切的神经编码 谐波声音的机制尚不清楚，最近的实验表明，谐波灵敏度为 在Marmoset的听觉皮层中广泛存在。由于皮质谐波灵敏度跨越多个八度，因此 它是通过结合通常只喜欢单个频率的皮质下输入来得出的。我们 建议通过同时记录许多 基于在线自适应刺激过程会自动探测的单个神经元 关于基础神经回路的显式计算模型。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般障碍。我们建议使用一个 谐波刺激设计的自适应在线方法。我们将采用两种类型的方法，一种是找到 最好驱动神经元的最佳刺激，另一种是基于模型的刺激设计 确定每个给定的模型，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）自动化系统以表征多个单个神经元的谐波灵敏度 使用Neuropixels记录探针，（2）自动化系统来表征谐波的听觉皮层层 使用两光子成像，在多个八度频率上的听觉皮层敏感性，（3） 在听觉皮层中有效编码谐波声音的生成电路模型。通过限制刺激 谐波声音足够复杂，但仍然可以处理，我们认为我们的方法更有可能 取得巨大的成功。我们在网上成功的几个成功中获得了有希望的初步结果 使用单单元记录的神经生理实验。将我们的在线方法扩展到Neuropixel 录制和两光子成像是合乎逻辑的下一步，有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 推断的电路模型，这些模型可能解释了谐波的高层皮质表示如何 来自更简单的输入组件，所有这些表示形式将在有效的背景下进行检查 自然声音的编码。