Statistical Signal Processsing Models of Electrosensory Acquisition

电传感采集的统计信号处理模型

• 批准号: 0078206
• 负责人: Mark Nelson
• 金额: $ 33.69万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2000
• 资助国家: 美国
• 起止时间: 2000-08-15 至 2004-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0078206&HistoricalAwards=false
• 关键词: Statistical; Signal; Processsing; Models; Electrosensory

The goal of this project is to understand how animals acquire and process sensory information about their environment. The focus is on identifying and characterizing brain mechanisms and informationprocessing principles that allow animals to enhance signals that are important to their behavior and to suppress irrelevant background noise. The specific studies are centered around the ability of weakly electricfish to detect and localize small prey in the dark using an active electric sense. While much is known about the neural circuitry in these animals, there is a gap in the theoretical understanding of how the brain should best process incoming sensory data. This proposal helps fill that gap by using statistical signal processing theory to develop optimal signal processing models of electrosensory target detection. These models are expected to provide a quantitative link between neurophysiology and behavior, and will provide valuable insights into the structural and functional organization of the nervous system. The issues addressed in these studies are of broad interest in sensory neurobiology, including the role of feedback pathways from higher brain centers, mechanisms for generating predictions of incoming sensory data, and synergistic interactions between sensory and motor aspects of sensory acquisition. In addition to advancing basic knowledge in neuroscience, the models of optimal sensory acquisition have relevance in applied areas of science and engineering such as artificial intelligence and robotics. This project also provides cross-disciplinary training for young scientists with interests that cut across the fields of physics, mathematics, computer science and neurobiology.

该项目的目标是了解动物如何获取和处理有关其环境的感官信息。重点是识别和表征大脑机制和信息处理原则，使动物能够增强对其行为重要的信号，并抑制不相关的背景噪音。 具体的研究集中在弱电鱼在黑暗中使用主动电感觉检测和定位小猎物的能力。虽然对这些动物的神经回路了解很多，但在理论上理解大脑应该如何最好地处理传入的感官数据方面存在差距。 这一建议有助于填补这一空白，利用统计信号处理理论开发最佳的信号处理模型的电感目标检测。 这些模型有望提供神经生理学和行为之间的定量联系，并将为神经系统的结构和功能组织提供有价值的见解。在这些研究中解决的问题是在感觉神经生物学的广泛兴趣，包括从更高的大脑中心的反馈通路的作用，产生传入的感觉数据的预测机制，和感官和运动方面的感官获得之间的协同作用。除了推进神经科学的基础知识外，最佳感觉获取的模型在科学和工程的应用领域（如人工智能和机器人）中也具有相关性。该项目还为青年科学家提供跨学科培训，这些科学家的兴趣涉及物理学、数学、计算机科学和神经生物学等领域。