Brain-Inspired Multimodal Navigation With Multiscale Hippocampal-Entorhinal Neural Network

The design of 3-D intelligent navigation system, which is accurate and robust as flying animals do, is an open challenge that can benefit from neural basis of spatial cognition of the brain. Here, we draw inspiration from the neural computation of multimodal and multiscale fusion of hippocampal place cells and entorhinal grid cells to develop a brain-inspired heterogeneous multimodal 3-D navigation framework for unmanned aerial vehicles (UAVs) in outdoor large environment. Multiscale place cell networks are constructed to represent external sensory cues with uncertainty. Multiscale recurrent grid cell networks with attractor dynamics are then introduced to integrate internal self-motion cues and feedforward inputs of place cells. Multiscale population vector decoding is then designed to read out the multiscale grid cells for positioning. Simulation and real data experiment results show the improved performance of the proposed method in accuracy and robustness compared to its conventional counterparts, displaying possible brain-inspired solution for navigation enhancement for UAVs.