Computational model of grid cells based on back‐propagation neural network

Abstract

To simulate the firing pattern of biological Grid cells (GCs), an efficient computational model based on back-propagation neural network (BPNN), which allows for the generation of regular hexagonal grids, is described. For each GC, it is associated with the output of a learned BPNN. The firing characteristics of GCs can be controlled by the adjustment factors (AFs) flexibly. During the vehicle's space exploration, GCs can exhibit hexagonal firing patterns with different characteristics according to the settings of the AFs. Simulation results validate the proposed model's accuracy as they are in agreement with GCs' biological experimental electroencephalogram results. Meanwhile, the application of the three-layer BPNN can greatly reduce the computational complexity, which lay a foundation for its application in realistic bio-inspired autonomous navigation systems.