  The Lobula Giant Movement Detector (LGMD) is an identified neuron of the
locust that detects looming objects and triggers the insect's escape responses.
Understanding the neural principles and network structure that lead to these
fast and robust responses can facilitate the design of efficient obstacle
avoidance strategies for 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 incorporates spiking frequency adaptation
and synaptic plasticity mechanisms, and which can be mapped onto existing
neuromorphic processor chips. However, as the model has a wide range of
parameters, and the mixed signal analogue-digital circuits used to implement
the model are affected by variability and noise, it is necessary to optimise
the parameters to produce robust and reliable responses. Here we propose to use
Differential Evolution (DE) and Bayesian Optimisation (BO) techniques to
optimise the parameter space and investigate the use of Self-Adaptive
Differential Evolution (SADE) to ameliorate the difficulties of finding
appropriate input parameters for the DE technique. We quantify the performance
of the methods proposed with a comprehensive comparison of different optimisers
applied to the model, and demonstrate the validity of the approach proposed
using recordings made from a DVS sensor mounted on a UAV.
