  The Lobula Giant Movement Detector (LGMD) is a an identified neuron of the
locust that detects looming objects and triggers its escape responses.
Understanding the neural principles and networks that lead to these fast and
robust responses can lead to the design of efficient facilitate obstacle
avoidance strategies in robotic applications. Here we present a neuromorphic
spiking neural network model of the LGMD driven by the output of a neuromorphic
Dynamic Vision Sensor (DVS), which has been optimised to produce robust and
reliable responses in the face of the constraints and variability of its mixed
signal analogue-digital circuits. As this LGMD model has many parameters, we
use the Differential Evolution (DE) algorithm to optimise its parameter space.
We also investigate the use of Self-Adaptive Differential Evolution (SADE)
which has been shown to ameliorate the difficulties of finding appropriate
input parameters for DE. We explore the use of two biological mechanisms:
synaptic plasticity and membrane adaptivity in the LGMD. We apply DE and SADE
to find parameters best suited for an obstacle avoidance system on an unmanned
aerial vehicle (UAV), and show how it outperforms state-of-the-art Bayesian
optimisation used for comparison.
