Integration of Optimality, Neural Networks, and Physiology for Field Studies of the Evolution of Visually-elicited Escape Behaviors of Orthoptera: A Minireview and Prospects

Sensing the approach of a predator is critical to the survival of prey, especially when the preyhas no choice but to escape at a precisely timed moment. Escape behavior has been approached from bothproximate and ultimate perspectives. On the proximate level, empirical research about electrophysiological mechanismsfor detecting predators has focused on vision, an important modality that helps prey to sense approachingdanger. Studies of looming-sensitive neurons in locusts are a good example of how the selective sensitivityof nervous systems towards specific targets, especially approaching objects, has been understood and realisticallymodeled in software and robotic systems. On the ultimate level, general optimality models have providedan evolutionary framework by considering costs and benefits of visually elicited escape responses. A recent papershowed how neural network models can be used to understand the evolution of visually mediated antipredatorybehaviors. We discuss this new trend towards integration of these relatively disparate approaches, theproximate and the ultimate perspectives, for understanding of the evolution of behavior of predators and prey.Focusing on one of the best-studied escape pathway models, the Orthopteran LGMD/DCMD pathway, wediscuss how ultimate-level optimality modeling can be integrated with proximate-level studies of escape behaviorsin animals.