Dragonflies have tiny brains and almost no depth perception, but still manage to capture around 95% of their prey, nabbing them in mid-air.

A researcher at the U.S. Department of Energy's Sandia National Laboratories is studying how those insects' brains can be so efficient at calculating complex trajectories. The insights might benefit missile defense guidance system development.

Dragonflies are efficient and their brains may help researchers develop better missile defense systems. Source: David WagmanIn computer simulations at the New Mexico laboratory, dragonflies in a simplified virtual environment successfully caught their prey using computer algorithms designed to mimic the way a dragonfly processes visual information while hunting.

The Sandia research is looking at whether dragonfly-inspired computing could improve missile defense systems. Those systems have the similar task of intercepting an object in flight. The dragonfly insights might help make on-board computers smaller without sacrificing speed or accuracy.

Computational neuroscientist Frances Chance developed the algorithms and presented her research at the International Conference on Neuromorphic Systems and at the Annual Meeting of the Organization for Computational Neurosciences.

Chance specializes in replicating biological neural networks — commonly known as brains — that require less energy and are better suited at learning and adapting than computers. Her work focuses on neurons, which are cells that send information through the nervous system.

Frances Chance, Sandia National Laboratory. Source: Randy Montoya“I try to predict how neurons are wired in the brain and understand what kinds of computations those neurons are doing, based on what we know about the behavior of the animal or what we know about the neural responses,” she said.

Blink of an eye

A dragonfly’s reaction time to a maneuvering prey is roughly 50 milliseconds. For reference, a human blink takes about 300 milliseconds. Fifty milliseconds allows only enough time for information to cross about three neurons. In other words, to keep up with a dragonfly, an artificial neural network needs to have completed processing information after three steps.

Missile defense systems rely on established intercept techniques that are, by contrast, computation-heavy. Chance said that rethinking those strategies using dragonflies as a model could potentially:

• Shrink the size, weight and power needs of onboard computers. Doing so would allow interceptors to be smaller and lighter, and therefore more maneuverable.
• Reveal new ways to intercept maneuvering targets such as hypersonic weapons, which follow less-predictable trajectories than ballistic missiles.
• Reveal new ways to home in on a target with less sophisticated sensors than are currently used.

Dragonflies and missiles move at different speeds, so it is unknown how well this initial research will translate to missile defense. But developing a computational model of a dragonfly brain also could have long-term benefits for machine learning and artificial intelligence, Chance said.

Chance's research is funded by Sandia’s Laboratory Directed Research and Development program.