Non-Linear Neuronal Responses as an Emergent Property of Afferent Networks: A Case Study of the Locust Lobula Giant Movement Detector
Figure 6
Trajectories and behavioural analysis of the “Strider” robot controlled by our LGMD model and a fly EMD based course stabilization system.
(A) Representative traces of the behaviour of the robot. Left panel shows the behaviour of the robot controlled by the LGMD model in combination with an EMD-based course stabilization system, as in [37]. Right panel shows the behaviour in absence of course stabilization. The blue traces indicate the position of the robot and the red segments indicate the detection of imminent collisions. The black dashed lines are obtained by fitting linear segments to the robot traces, minimizing the Mean Square Error (MSE). Inserted into both panels are polar plots of the heading direction. (B) Collision detections between 20cm and 100cm away from the wall were classified as correct, those detected closer than 20cm from the wall (solid gray area in A) as missed, and collisions detected at a distance over 100cm as false positives (dashed area in A). (C) Detected collisions vs. distance. Bar colors correspond to the classification of the collision detection defined in panel B. (D) Segment length: Histogram of the length of the linear segments identified with the fitting procedure in (A). This measures the straightness of the traces of the robot in the control situation and when controlled by the combined course stabilization and collision avoidance system. The error bars indicate data variance. The data in (B) and (C) corresponds to 16 experiments with the combined course stabilization and collision avoidance system and 5 control experiments (LGMD alone) in (D).