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Frances Chance – Modeling Coordinate Transformations in Neural and Neuromorphic Systems
5 May, 2023 @ 11:00 - 12:00 EDT
Hosted by the Perception and Robotics Group Seminar Series on Robotics and Computer Vision at the University of Maryland.
Abstract. Animals excel at a wide range behaviors, many of which are essential for survival. For example, dragonflies are aerial predators, known for both their speed and high success rate, that must perform fast, accurate, and efficient calculations to survive. I will present a neural network model, inspired by the dragonfly nervous system, that calculates turning for successful prey interception. The model relies upon a coordinate transformation from eye-coordinates to body-coordinates, an operation that must be performed by almost any animal nervous system relying upon sensory information to interact with the external world. I will discuss how I and collaborators are combining neuroscience experiments, modeling studies, and exploration of neuromorphic architectures to understand how the biological dragonfly nervous system performs coordinate transformations and to develop novel approaches for efficient neural- inspired computation.
Bio. As a computational neuroscientist, Frances Chance has always been fascinated by how neural circuits compute information. Her current research focuses on applying knowledge of how neural systems operate towards the development of novel neuro-inspired algorithms and brain- based architectures. Frances Chance received her PhD and MS from Brandeis University and her BS from the California Institute of Technology. Currently she is a Principal Member of the Technical Staff at Sandia National Laboratories.