Using a mobile robot to study locust collision avoidance responses.
The visual systems of insects perform complex processing using remarkably compact neural circuits, yet these circuits are often studied using simplified stimuli which fail to reveal their behaviour in more complex visual environments. We address this issue by testing models of these circuits in real-world visual environments using a mobile robot. In this paper we focus on the lobula giant movement detector (LGMD) system of the locust which responds selectively to objects which approach the animal on a collision course and is thought to trigger escape behaviours. We show that a neural network model of the LGMD system shares the preference for approaching objects and detects obstacles over a range of speeds. Our results highlight aspects of the basic response properties of the biological system which have important implications for the behavioural role of the LGMD.
Blanchard
M
M
Institute of Neuroinformatics, ETH-University Zürich, Switzerland.
Verschure
P F
PF
Rind
F C
FC
D000818:Animals; D001362:Avoidance Learning; D004924:Escape Reaction; D006110:Grasshoppers / Q000502:physiology*; D008124:Locomotion; D008959:Models, Neurological; D009039:Motion Perception; D009475:Neurons, Afferent / Q000502:physiology; D012371:Robotics / Q000295:instrumentation* / Q000379:methods*