CRCNS: Coding for optimal performances in natural environments

CRCNS：自然环境中最佳性能的编码

• 批准号: 8680204
• 负责人: Brian J Fischer
• 金额: $ 36.05万
• 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 2017-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8680204
• 关键词: Accounting; Acoustics; Address; Auditory; Auditory system; Barn Owls; Bayesian Analysis; Bayesian Modeling; Bayesian Prediction; Behavior; Behavioral; Brain; Cells; Code; Collaborations; Comb animal structure; Commit; 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 Minority; Universities; Width; Work; base; behavior test; 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最佳适应环境至关重要。这个建议调查这个问题，问大脑如何可以接近统计最优的声音定位系统谷仓猫头鹰。贝叶斯理论框架将被用来描述如何感官和先验信息可以最佳地结合起来，以指导定向行为。具体来说，我们试图证明，感官的可靠性和先验信息表示在响应特性和地形的神经人口，代表听觉空间。第一个目的是研究感觉线索可靠性在大脑中的表现。感官信息的最佳使用要求感官线索的统计可靠性是从神经反应中获得的。以往的理论认为，线索的可靠性是编码在神经反应的增益或神经反应的选择性，但如何可靠性表示是未知的。在猫头鹰中，空间线索的统计可靠性的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，可靠性是编码在调谐曲线宽度的空间特定的神经元位于 猫头鹰的中脑我们将独立地操纵调谐曲线宽度和发射率来检验这一假设，并用行为来检验模型。第二个目标是研究声音定位的空间线索整合是否遵循统计最优性规则。在自然环境中的感知往往取决于多种线索的整合，无论是在模态内还是跨模态。在这里，整合是线性还是非线性是至关重要的，因为将贝叶斯模型从一维扩展到二维表明条件独立的感觉线索的最佳组合应该是非线性的。在猫头鹰的大脑中，用于确定仰角和方位角的空间线索是独立处理的，并在中脑中非线性地组合形成空间感受野。然而，声音定位线索是否是条件独立的是未知的。这个目标将展示为什么非线性操作是必不可少的最佳线索组合，以及它们是如何产生的。我们将进行体内细胞内记录和行为测试来解决这些问题。这将提供一个实验测试的预测，条件独立线索的最佳组合是非线性的。第三个目标是将该模型扩展到编码动态听觉场景，时间维度将被纳入贝叶斯模型的声音定位。我们将使用群体向量模型来确定神经系统如何通过贝叶斯推理在听觉空间中实现预测能力。我们将测量中脑神经元在空间和时间上的感受野，以检验猫头鹰对朝向注视中心移动的源有偏见的假设。我们将使用行为测试来测量移动声源的检测阈值。最后，我们将研究在非均匀网络中的动态增益控制是否可以解释声音运动的贝叶斯预测编码，并对朝向注视中心移动的源有偏见。 更广泛的影响：关于自然场景的统计数据如何被神经编码捕获的悬而未决的问题包括感官信息的可靠性如何被表示并与先验概率知识相结合，以及感官线索如何被整合以最佳地引导行为。这个项目解决了这些问题的猫头鹰的听觉中脑空间的异质性表示。非均匀表示是否可以使用种群向量来解码以执行贝叶斯推理，并且这种机制在多个维度上工作，这超越了仓鸮的声音定位，成为神经编码的普遍兴趣。参与这个项目的PI，其中一个是初级研究员，收集建模，生理学和行为方法的互补专业知识，允许真正的跨学科方法。因此，该项目将巩固强大的合作，同时提供关于神经科学中悬而未决问题的突破性信息。这三个机构致力于培训代表性不足的群体。阿尔伯特·爱因斯坦医学院位于布朗克斯，使其成为该国最多样化和最贫穷的县之一的发展极，并为直接获得转化研究提供了可能。列入数学系在西雅图大学，排名前十的大学在西部的本科课程，和俄勒冈州大学将确保该项目将加强培训从本科到博士后水平。