Statistical Signal Processsing Models of Electrosensory Acquisition

这个项目的目标是了解动物是如何获得和处理有关其环境的感官信息的。重点是识别和描述大脑机制和信息处理原理，这些机制和信息处理原理允许动物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在黑暗中使用主动电感探测和定位小型猎物的能力。虽然对这些动物的神经回路有很多了解，但对于大脑如何最好地处理传入的感觉数据，理论上的理解存在差距。该方案利用统计信号处理理论，建立了电感目标检测的最优信号处理模型，从而填补了这一空白。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领域引起了广泛的兴趣，包括来自高级大脑中心的反馈通路的作用，产生对传入感觉数据的预测的机制，以及感觉获得的感觉和运动方面之间的协同作用。除了提高神经科学的基础知识外，最优感觉获得的模型在人工智能和机器人等科学和工程应用领域也具有相关性。该项目还为年轻科学家提供跨学科培训，他们的兴趣涉及物理、数学、计算机科学和神经生物学等领域。

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.