CRCNS: Coding for optimal performances in natural environments

CRCNS：自然环境中最佳性能的编码

• 批准号: 9095305
• 负责人: Brian J Fischer
• 金额: $ 35.5万
• 依托单位: ALBERT EINSTEIN COLLEGE OF MEDICINE, INC
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-07-01 至 2018-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9095305
• 关键词: Accounting; Acoustics; Address; Auditory; Auditory system; Barn Owls; Bayesian Analysis; Bayesian Modeling; Bayesian Prediction; Behavior; Behavioral; Binaural; Brain; Cells; Code; Collaborations; Comb animal structure; Country; County; Cues; Data; Dependence; Detection; Development; Dimensions; Discrimination; Ensure; Environment; Institution; Knowledge; Location; Mathematics; Measures; Medicine; Midbrain structure; Modality; Modeling; Motion; Nature; Neurons; Neurosciences; Noise; Oregon; Pathway interactions; Perception; Performance; Physiology; Population; Process; Property; Reflex action; Research Personnel; Sensory; Signal Transduction; Sound Localization; Source; Strigiformes; Structure; System; Testing; Time; Training; Transcend; Translational Research; Underrepresented Groups; Universities; Width; Work; base; behavior test; behavioral response; college; gaze; in vivo; interdisciplinary approach; interest; motion sensitivity; operation; pressure; programs; receptive field; relating to nervous system; research study; response; skills; sound; spatial integration; statistics; theories; tool; vector

DESCRIPTION (provided by applicant): Capturing nature's statistical structure in the neural coding is essential for optimal adaptation to the environment. This proposal investigates this issue by asking how the brain can approach statistical optimality in the sound localization system of barn owls. A Bayesian theoretical framework will be used to describe how sensory and a priori information can be combined optimally to guide orienting behavior. Specifically, we seek to demonstrate that sensory reliability and a priori information are represented in the response properties and topography of the neural population that represents auditory space. The first aim studies how sensory cue reliability is represented in the brain. Optimal use of sensory information requires that the statistical reliability of sensory cues is accessible from neural responses. Previous theories have suggested that cue reliability is encoded in the gain of neural responses or alternatively the selectivity of neural responses but how reliability is represented is not known. In the owl, changes in the statistical reliability of spatial cues resultin changes in sound localization behavior consistent with a Bayesian model. Our model predicts that the reliability is encoded in the tuning curve widths of space-specific neurons located in the owl's midbrain. We will manipulate tuning-curve widths and firing rates independently to test this hypothesis and test the model with behavior. The second aim will study whether the integration of spatial cues for sound localization follows the rules of statistical optimality. Perception in natural environments often depends on the integration of multiple cues, both within modalities and across modalities. Here, whether the integration is linear or nonlinear is crucial, as extending a Bayesian model from one to two dimensions indicates that optimal combination of conditionally independent sensory cues should be nonlinear. In the owl's brain, the spatial cues used to determine elevation and azimuth are processed independently and combined nonlinearly in the midbrain to form spatial receptive fields. However, whether or not sound localization cues are conditionally independent is unknown. This aim will demonstrate why nonlinear operations are essential for optimal cue combination and how they arise. We will perform in vivo intracellular recording and behavioral tests to address these questions. This will provide an experimental test of the prediction that optimal combination of conditionally independent cues is nonlinear. The third aim will extend the model to coding dynamic auditory scenes; the time dimension will be incorporated into the Bayesian model of sound localization. We will use a population vector model to determine how a neural system can achieve predictive power in auditory space through Bayesian inference. We will measure receptive fields of midbrain neurons in space and time to test the hypothesis that the owl has a bias for sources moving toward the center of gaze. We will use behavioral tests to measure detection thresholds for moving sound sources. Finally, we will study whether a dynamic gain control in a non-uniform network can account for Bayesian predictive coding of sound motion with a bias for sources moving toward the center of gaze. Broader Impacts: Outstanding open questions of how statistics of natural scenes are captured by neural coding include how reliability of sensory information is represented and combined with prior probabilistic knowledge, and how sensory cues are integrated to optimally guide behavior. This project addresses these questions in the heterogeneous representation of space of the owl's auditory midbrain. Whether non-uniform representations can be decoded using a population vector to perform Bayesian inference and that this mechanism works in multiple dimensions transcends sound localization in barn owls, becoming of general interest to neural coding. The PIs involved in this project, one of them a junior researcher, gather complementary expertise in modeling, physiology and behavioral approaches allowing for a truly interdisciplinary approach. This project will thus consolidate a powerful collaboration while providing groundbreaking information on outstanding questions in Neuroscience. The three institutions involved are committed to the training of underrepresented groups. The location of the Albert Einstein College of Medicine in the Bronx, makes it a pole of development in one of the most diverse and poor counties in the country and provides the potential for direct access to translational research. The inclusion of the Department of Mathematics at Seattle University, ranked among the top ten universities in the West for undergraduate programs, and the University of Oregon will ensure that this project will enhance training from the undergraduate to postdoctoral levels.

描述（由申请人提供）：在神经编码中捕获自然的统计结构对于对环境的最佳适应至关重要。该提案通过询问大脑如何在谷仓猫头鹰的声音定位系统中达到统计最佳性来调查此问题。贝叶斯理论框架将用于描述如何最佳地组合感觉和先验信息以指导定向行为。具体而言，我们试图证明感官可靠性和先验信息在代表听觉空间的神经种群的响应属性和地形中表示。第一个目的研究了大脑中感觉提示可靠性如何表示。感觉信息的最佳使用要求从神经反应中获得感觉线索的统计可靠性。以前的理论表明，提示可靠性是在神经反应的获得或神经反应的选择性中编码的，但尚不清楚可靠性。在猫头鹰中，空间提示的统计可靠性变化导致声音定位行为的变化与贝叶斯模型一致。我们的模型预测，可靠性是在位于空间特异性神经元的调谐曲线宽度中编码的 猫头鹰的中脑。我们将独立测试这一假设并通过行为测试模型来操纵调谐曲线宽度和发射速率。第二个目的将研究声音定位的空间提示是否遵循统计最优性规则。自然环境中的感知通常取决于在模式和跨模态内的多个提示的整合。在这里，无论是线性还是非线性是至关重要的，因为将贝叶斯模型从一个维度扩展到二维表明，有条件独立的感觉提示的最佳组合应非线性。在猫头鹰的大脑中，用于确定抬高和方位角的空间提示是独立处理和非线性处理的，以形成空间接受场。但是，声音定位线索是否有条件地独立。这个目标将证明为什么非线性操作对于最佳提示组合及其出现是必不可少的。我们将执行体内细胞内记录和行为测试以解决这些问题。这将提供一个实验测试，以预测有条件独立的提示是非线性的。第三个目标将将模型扩展到编码动态听觉场景。时间维度将纳入声音定位的贝叶斯模型中。我们将使用人群矢量模型来确定神经系统如何通过贝叶斯推断在听觉空间中实现预测能力。我们将在时空中测量中脑神经元的接受场，以测试猫头鹰对朝心凝视中心的源偏差的假设。我们将使用行为测试来测量移动声源的检测阈值。最后，我们将研究非均匀网络中的动态增益控制是否可以解释贝叶斯预测性运动的编码，并偏向于朝向凝视中心的源。 更广泛的影响：神经编码如何捕获自然场景的统计数据的杰出开放问题包括如何代表感官信息的可靠性，并与先前的概率知识相结合，以及如何整合感官提示以最佳指导行为。该项目在猫头鹰听觉中脑空间的异质表示中解决了这些问题。是否可以使用种群矢量进行贝叶斯推论来解码不均匀的表示，并且该机制在多个维度上起作用，超越了谷仓猫头鹰的声音定位，成为神经编码的一般兴趣。参与该项目的PI，其中一个是初级研究人员，在建模，生理和行为方法方面收集了互补的专业知识，从而实现了真正的跨学科方法。因此，该项目将巩固强大的合作，同时提供有关神经科学中杰出问题的开创性信息。所涉及的三个机构致力于培训代表性不足的群体。阿尔伯特·爱因斯坦医学院在布朗克斯的位置使其成为该国最多样化和最贫穷的县之一的发展杆，并为直接进入转化研究提供了潜力。西雅图大学数学系的包括在本科课程的西部十大大学中，俄勒冈大学将确保该项目将增强本科生从本科到博士后水平的培训。