Statistical Signal Processsing Models of Electrosensory Acquisition

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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.