Modeling Coordinate Transformations in Neural and Neuromorphic Systems
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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.
https://www.sandia.gov/ccr/staff/frances-s-chance/
Math, 501 and Zoom: https://arizona.zoom.us/j/81337180102 Password: applied