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最佳地适应环境至关重要。该提案通过询问大脑如何在仓鸮的声音定位系统中达到统计最优性来研究这个问题。贝叶斯理论框架将用于描述如何将感官信息和先验信息最佳地结合起来以指导定向行为。具体来说，我们试图证明感觉可靠性和先验信息在代表听觉空间的神经群体的响应属性和拓扑中得到体现。第一个目标是研究大脑中如何表征感觉线索的可靠性。感觉信息的最佳使用要求可以从神经反应中获得感觉线索的统计可靠性。先前的理论表明，线索可靠性被编码在神经反应的增益中，或者神经反应的选择性中，但可靠性是如何表示的尚不清楚。在猫头鹰中，空间线索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，可靠性被编码在位于空间特定神经元的调谐曲线宽度中。 猫头鹰的中脑。我们将独立地操纵调整曲线宽度和发射率来测试这个假设并测试模型的行为。第二个目标将研究声音定位的空间线索的整合是否遵循统计最优性规则。自然环境中的感知通常取决于多种线索的整合，无论是在模式内还是跨模式。在这里，积分是线性的还是非线性的至关重要，因为将贝叶斯模型从一维扩展到二维表明条件独立的感官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被独立处理，并在中脑中非线性组合以形成空间感受野。然而，声音定位线索是否有条件独立尚不清楚。这一目标将证明为什么非线性操作对于最佳提示组合至关重要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。这将为条件独立线索的最佳组合是非线性的预测提供实验测试。第三个目标是将模型扩展到动态听觉场景的编码；时间维度将被纳入声音定位的贝叶斯模型中。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凝视中心移动的来源有偏见的假设。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，以及朝向凝视中心移动的源的偏差。 更广泛的影响：关于如何通过神经编码捕获自然场景统计数据的悬而未决的问题包括如何表示感官信息的可靠性并与先验概率知识相结合，以及如何整合感官线索以最佳地指导行为。该项目通过猫头鹰听觉中脑空间的异质表征来解决这些问题。是否可以使用群体向量来解码非均匀表示以执行贝叶斯推理，以及该机制在多个维度上的工作超越了谷仓猫头鹰的声音定位，成为神经编码的普遍兴趣。参与该项目的 PI（其中一名初级研究员）收集了建模、生理学和行为方法方面的互补专业知识，从而实现了真正的跨学科方法。因此，该项目将巩固强有力的合作，同时提供有关神经科学领域悬而未决问题的开创性信息。所涉及的三个机构致力于对代表性不足的群体进行培训。阿尔伯特·爱因斯坦医学院位于布朗克斯区，使其成为美国最多元化和最贫困的县之一的发展极点，并为直接进行转化研究提供了潜力。本科项目排名西方前十的西雅图大学数学系和俄勒冈大学的加入，将确保该项目加强从本科生到博士后水平的培养。