Statistical Signal Processsing Models of Electrosensory Acquisition

这个项目的目标是了解动物如何获取和处理关于环境的感官信息。重点是识别和描述大脑机制和信息处理原理，使动物能够增强对其行为重要的信号，并抑制无关的背景噪音。具体的研究集中在弱电鱼利用主动电感在黑暗中探测和定位小型猎物的能力上。虽然人们对这些动物的神经回路了解很多，但在大脑如何最好地处理传入的感觉数据的理论理解方面存在空白。本文利用统计信号处理理论建立了电感觉目标检测的最佳信号处理模型，填补了这一空白。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。这些研究中涉及的问题在感觉神经生物学中具有广泛的兴趣，包括来自高级脑中心的反馈通路的作用，对传入的感觉数据产生预测的机制，以及感觉获取的感觉和运动方面之间的协同相互作用。除了推进神经科学的基础知识外，最佳感觉获取模型还与人工智能和机器人技术等科学和工程应用领域相关。该项目还为有兴趣跨越物理、数学、计算机科学和神经生物学领域的年轻科学家提供跨学科培训。

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.