CRCNS: Coding for optimal performances in natural environments

CRCNS：自然环境中最佳性能的编码

• 批准号: 8494034
• 负责人: Brian J Fischer
• 金额: $ 34.49万
• 依托单位: ALBERT EINSTEIN COLLEGE OF MEDICINE
• 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/8494034
• 关键词: Accounting; Acoustics; Address; Auditory; Auditory system; Barn Owls; 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; 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）：在神经编码中捕捉自然的统计结构对于优化适应环境至关重要。本提案通过询问大脑如何在仓鸮的声音定位系统中实现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指导定向行为。具体来说，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群体的响应特性和地形中得到表征。第一个目标是研究感觉线索的可靠性如何在大脑中表现出来。感官信息的最佳使用要求感官线索的统计可靠性可以从神经反应中获得。先前的理论认为，线索的可靠性编码在神经反应的增益中，或者是神经反应的选择性中，但可靠性是如何表现的尚不清楚。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，这与贝叶斯模型一致。我们的模型预测，可靠性是编码在调谐曲线宽度的空间特异性神经元位于