CRCNS: Visual Modulation of Panoramic Auditory Spatial Processing

CRCNS：全景听觉空间处理的视觉调制

基本信息

批准号：
10645054
负责人：
Yi Zhou
金额：
$ 37.98万
依托单位：
ARIZONA STATE UNIVERSITY-TEMPE CAMPUS
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-07-01 至 2025-06-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10645054
关键词：
Action Potentials Alpha Rhythm Area Attention Auditory Auditory Perception Auditory area Back Benchmarking Beta Rhythm Biophysics Brain Callithrix Classification Code Communication Complex Computer Models Cues Data Data Analyses Environment Eye Face Feedback Generations Goals Hearing Human Impairment Instruction Linear Models Location Maps Modality Modeling Monkeys Nervous System Neurons Outcome Output Perception Phase Photic Stimulation Physical environment Population Population Distributions Positioning Attribute Programming Languages Property Research Restaurants Sensory Social Environment Source Space Perception Specificity Standardization Statistical Models Stimulus Techniques Theoretical Studies Validation Vision Visual Visual Fields Visuospatial Work cell type data sharing experience experimental study extracellular hearing impairment improved multisensory neural neural circuit neural network neuromechanism neurophysiology preference response sensory input sensory integration simulation sound source localization theories visual information vocalization

项目摘要

In everyday activity, our sense of space is guided by coordinated multisensory analyses of sensory information from the surrounding environment. Multisensory spatial information is critical for identifying and attending to a target sound in a noisy environment (e.g., night bars, restaurants). Multisensory information is also crucial for detecting heard but unseen dangers because the spaces encoded by sounds and sights do not always align. For foveal species like humans and monkeys, the visual field is restricted to the frontal space, whereas the auditory field is panoramic, covering the entire frontal and rear space. The rear space, however, has been largely overlooked in multisensory research. It remains largely unknown where and how vision directly influences auditory spatial processing in the brain. The long-term objective of this study is to understand the fundamental strategies of multisensory spatial perception and cortical neural mechanisms that implement these strategies in the brain. This proposal will investigate how visual information modulates auditory encoding of 360-degree, panoramic space in auditory cortex using an integrated approach based on neurophysiology, mechanistic computational modeling, and predictive statistical modeling. We hypothesize that visuo-spatial information increases auditory representation of the frontal space by changing the directional preference of neural network dynamics. Neurophysiological experiments will provide a comprehensive assessment of changes in the 360-degree spatial tuning of auditory cortex neurons after frontal visual stimulation. Computational models will aid in identifying putative cell types and reveal how heterogeneous recorded extracellular spiking waveforms depend on stimulus conditions and cell type. Predictive statistical modeling will determine the sources of variance in cortical neuron spiking data and will predict spiking output of different cell types under different conditions, all with laminar specificity. This integrated approach will provide an understanding of visual modulation of auditory spatial processing with a focus on the layer-specific interactions between local rhythm generators and single unit activity. The impact of this work will be maximized through sharing of data in standardized formats, rigorous and transparent model validation, and use of model description standards, which allows for code generation for simulating models in many different programming languages or simulation platforms for model re-use. RELEVANCE (See instructions): The ability of the nervous system to integrate multisensory inputs is essential to communications in complex sensory and social environments. Impairment of this ability is the most noticeable outcome of hearing loss. Identifying how the auditory cortex encodes sound features in a visual environment will improve our understanding of how multisensory perception might be implemented in neural circuits, thereby revealing potential sources of perceptual impairment in real-world conditions.

在日常活动中，我们的空间感是由感官的协调多感官分析引导的。来自周围环境的信息。多感官空间信息对于识别和识别至关重要关注嘈杂环境中的目标声音（例如夜间酒吧、餐馆）。多感官信息对于检测听到但看不见的危险也至关重要，因为空间由声音和景象编码不要总是对齐。对于人类和猴子等中央凹物种，视野仅限于额叶空间，而听觉场是全景式的，覆盖整个前部和后部空间。后排空间，然而，在多感官研究中，这一点在很大程度上被忽视了。目前仍不清楚在哪里以及如何视觉直接影响大脑的听觉空间处理。这项研究的长期目标是了解多感官空间感知和皮质神经机制的基本策略在大脑中实施这些策略。该提案将研究视觉信息如何调节使用基于集成方法的听觉皮层 360 度全景空间的听觉编码神经生理学、机械计算模型和预测统计模型。我们假设视觉空间信息通过以下方式增加额叶空间的听觉表征：改变神经网络动力学的方向偏好。神经生理学实验将提供听觉皮层 360 度空间调谐变化的全面评估额叶视觉刺激后的神经元。计算模型将有助于识别假定的细胞类型和揭示异质记录的细胞外尖峰波形如何依赖于刺激条件和细胞类型。预测统计模型将确定皮质神经元尖峰的方差来源数据，并将预测不同条件下不同细胞类型的尖峰输出，所有这些都具有层流特异性。这种综合方法将提供对听觉空间的视觉调制的理解处理重点是局部节奏发生器和单个单元之间特定于层的交互活动。这项工作的影响将通过以标准化格式、严格的数据共享来最大化透明的模型验证，以及使用模型描述标准，允许代码生成多种不同编程语言或仿真平台的模型仿真模型重用。相关性（参见说明）：神经系统整合多感官输入的能力对于沟通至关重要复杂的感官和社会环境。这种能力的损害是最明显的结果听力损失。识别听觉皮层如何在视觉环境中编码声音特征将提高我们对如何在神经回路中实现多感官知觉的理解，从而揭示现实世界条件下知觉障碍的潜在来源。

项目成果

期刊论文数量（4）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Editorial: Listening with two ears - new insights and perspectives in binaural research.

社论：用两只耳朵聆听——双耳研究的新见解和观点。

DOI：
发表时间：
2023
期刊：
影响因子：
0
作者：
Zhang, Huiming;Zhou, Yi;Reiss, Lina
通讯作者：
Reiss, Lina

Combining hypothesis- and data-driven neuroscience modeling in FAIR workflows.

在 FAIR 工作流程中结合假设和数据驱动的神经科学建模。

DOI：
发表时间：
2022-07-06
期刊：
影响因子：
7.7
作者：
Eriksson, Olivia;Bhalla, Upinder Singh;Blackwell, Kim T;Crook, Sharon M;Keller, Daniel;Kramer, Andrei;Linne, Marja;Saudargienė, Ausra;Wade, Rebecca C;Hellgren Kotaleski, Jeanette
通讯作者：
Hellgren Kotaleski, Jeanette

NeuroML-DB: Sharing and characterizing data-driven neuroscience models described in NeuroML.

NeuroML-DB：共享和表征 NeuroML 中描述的数据驱动的神经科学模型。

DOI：
发表时间：
2023-03
期刊：
影响因子：
4.3
作者：
Birgiolas, Justas;Haynes, Vergil;Gleeson, Padraig;Gerkin, Richard C;Dietrich, Suzanne W;Crook, Sharon
通讯作者：
Crook, Sharon

Discovering optimal features for neuron-type identification from extracellular recordings.

从细胞外记录中发现神经元类型识别的最佳特征。