Shaping the Ultra-Selectivity of a Looming Detection Neural Network from Non-linear Correlation of Radial Motion

In this paper, a numerical neural network inspired by the lobula plate/lobula columnar type II (LPLC2), the ultra-selective looming sensitive neurons identified within visual system of Drosophila, is proposed utilising non-linear computation. This method aims to be one of the explorations towards solving the collision perception problem resulted from radial motion. Taking inspiration from the distinctive structure and placement of directionally selective neurons (DSNs) named T4/T5 interneurons and their post-synaptic neurons, the motion opponency along four cardinal directions is computed in a non-linear way and subsequently mapped into four quadrants. More precisely, local motion excites adjacent neurons ahead of the ongoing motion, whilst transfers inhibitory signals to presently-excited neurons with slight temporal delay. From comparative experimental results collected, the main contribution is established by sculpting the ultra-selective features of generating a vast majority of responses to dark centroid-emanated centrifugal motion patterns whilst remaining nearly silent to those starting from other quadrants of receptive field (RF). The proposed method also distinguishes relatively dark approaching objects against brighter background and light ones against dark background via exploiting ON/OFF parallel channels, which well fits the physiological findings. Accordingly, the proposed neural network consolidates the theory of non-linear computation in Drosophila's visual system, a prominent paradigm for studying biological motion perception. This research also demonstrates potential of being fused with attention mechanism towards utility in devices such as unmanned aerial vehicles (UAVs), protecting them from unexpected and imminent collision by calculating a safer flying pathway.