Ting Feng: Visually guided behavior in mice
Title
Visually guided behavior in mice
Mentor
Department
Biosketch
Ting Feng, Ph.D., is a research assistant professor in the Department of Biology. Feng has extensive experience working with rodent models in neuroscience throughout her academic and research journey. She has a broad background in neurobiology and molecular biology, with a more recent focus on the neural circuit basis of behavior and developmental plasticity. Her technical expertise is in critical neural circuit analysis approaches, including AAV preparation and application, confocal imaging and quantification, stereotaxic rodent brain surgery and behavior analysis. Feng’s current research explores the neural circuitry underlying visual-guided prey-capture behavior, aiming to understand how the brain processes visual information to direct hunting and foraging behavior. Currently, she is particularly interested in how experience coupled to specific internal states shapes visual processing in mature adults.
Project overview
Our current project is to establish a basic understanding of how leptin signaling impacts visual attention and orienting behavior via leptin-receptor positive projections to the superior colliculus (SC). Leptin, a hormone primarily produced in the fat cells of the body, plays a critical role in regulating energy balance. However, how these leptin levels relate to the visual orienting behaviors that are important for food foraging and resource acquisition remains unclear. Leptin overall reliably reduces feeding and food intake via specific neuron types and circuits located in a distinct subset of hypothalamic nuclei, which is a critical brain region involved in regulating internal state, including hunger, fear and aggression. Our project goal is to quantify how leptin signaling alters adaptive orienting to visual cues with distinct valence. Parallelly, we are also investigating sexual dimorphism in visually guided approach and escape behavior. In this project, student researchers will work with laboratory mice, gaining hands-on experience in behavior studies. The student will then assist with scoring the mice’s behavior performance from video recordings and apply DeepLabCut, a deep-learning-based tool for pose estimation. Alternatively, students can work on confocal imaging and quantification to understand how neuron synapses are distributed within the neural circuit in our mouse model.
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