Modeling, Computation, Nonlinerarity, Randomness and Waves Seminar

While dragonflies are well-known for their high success rates when hunting prey, how the underlying neural circuitry generates the prey-interception trajectories used by dragonflies to hunt remains an open question.  I present a model of dragonfly prey interception that uses a neural network to calculate motor commands for prey-interception.  The model uses the motor outputs of the neural network to internally generate a forward model of prey-image translation resulting from the dragonfly's own turning that can then serve as a feedback guidance signal, resulting in trajectories with final approaches very similar to proportional navigation. The neural network is biologically-plausible and can therefore can be compared against in vivo neural responses in the biological dragonfly, yet parsimonious enough that the algorithm can be implemented without requiring specialized hardware.

Zoom:  https://arizona.zoom.us/j/94534134312   Password:  “arizona” (all lower case)