To derive meaningful navigation strategies, animals have to estimate their directional headings in the environment. Accordingly, this function is achieved by the head direction cells that were found in mammalian brains, whose neural activities encode one’s heading direction. It is believed that such head direction information is generated by integrating self-motion cues, which also introduces accumulative errors in the long term. To eliminate such errors, this paper presents an efficient calibration model that mimics the animals’ behavior by exploiting visual cues in a biologically plausible way, and then implements it in robotic navigation tasks. The proposed calibration model allows the agent to associate its head direction and the perceived egocentric direction of a visual cue with its position and orientation, and therefore to calibrate the head direction when the same cue is viewed again. We examine the proposed head direction calibration model in extensive simulations and real-world experiments and demonstrate its excellent performance in terms of quick association of information to proximal or distal cues as well as accuracy of calibrating the integration errors of the head direction. Videos can be viewed at https://videoviewsite.wixsite.com/hdc-calibration.

The medial entorhinal cortex of rodents is known to contain grid cells that exhibit precise periodic firing patterns based on the animal’s position, resulting in a distinct hexagonal pattern in space. These cells have been extensively studied due to their potential to unveil the navigational computations that occur within the mammalian brain and interesting phenomena such as so-called grid cell distortions have been observed. Previous neuronal models of grid cells assumed their firing fields were independent of environmental boundaries. However, more recent research has revealed that the grid pattern is, in fact, dependent on the environment’s boundaries. When rodents are placed in nonsquare cages, the hexagonal pattern tends to become disrupted and adopts different shapes. We believe that these grid cell distortions can provide insights into the underlying neural circuitry involved in grid cell firing. To this end, a calibration circuit for grid cells is proposed. Our simulations demonstrate that this circuit is capable of reproducing grid distortions observed in several previous studies. Our model also reproduces distortions in place cells and incorporates experimentally observed distortions of speed cells, which present further opportunities for exploration. It generates several experimentally testable predictions, including an alternative behavioral description of boundary vector cells that predicts behaviors in nonsquare environments different from the current model of boundary vector cells. In summary, our study proposes a calibration circuit that reproduces observed grid distortions and generates experimentally testable predictions, aiming to provide insights into the neural mechanisms governing spatial computations in mammals.