Parameter optimization and learning in a spiking neural network for UAV obstacle avoidance targeting neuromorphic processors

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 leads 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 analog-digital circuits used to implement the model are affected by variability and noise, it is necessary to optimize the parameters to produce robust and reliable responses. Here, we propose to use differential evolution (DE) and Bayesian optimization (BO) techniques to optimize the parameter space and investigate the use of self-adaptive DE (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 optimizers applied to the model and demonstrate the validity of the approach proposed using recordings made from a DVS sensor mounted on an unmanned aerial vehicle (UAV)