Neuromorphic object tracking architecture, based on compound eyes, and implementation on FPGA

Recent findings in neuroscience, show that rapid changes in flight direction of a housefly/blowfly (mainly to track objects) are attributable to neural circuits distributed behind its photo-receptors. While tracking objects, using its compound eye structure, a fly is able to detect changes in the motion of the object quickly and changes its own motion accordingly. The working of these neural circuits may be modelled as a set of leaky integrate and fire neurons connected in a special manner to form a competitive feedback control. Based on this knowledge, we present a neuromorphic competitive control circuit utilizing an inference neuron model to control N actuators and analyze their outputs for tracking an object. This model was simulated in software first and then implemented on a Xilinx Artix-7 XC7A35T- ICPG236C FPGA board using Verilog. The results show an observable decoherence phenomenon between the neurons and support the working principle of the model