Grid cells and place cells have shown to play an important role in spatial cognition in rats. While place cells provide global localization from external environment information, grid cells provide a “neural odometry” for path integration from internal vestibular information. In this paper we describe a spatial cognition model integrating grid cells and place cells from behavioral and neurophysiological brain studies in rats. Grid cell firing is generated from a linear oscillatory interference model that includes a reset mechanism to overcome errors in “neural odometry” readings. The model is evaluated in simulation. Future work is discussed including extensions to the model and evaluation under physical robots.

The study of spatial memory and learning in rats has inspired the development of multiple computational models that have lead to novel robotics architectures. Evaluation of computational models and resulting robotic architecture is usually carried out at the behavioral level by evaluating experimental tasks similar to those performed with rats. While multiple metrics are defined to evaluate behavioral performance in rats, metrics for robot task evaluation are very limited mostly to success/failure and time to complete task. In this paper we present a set of metrics taken from rat spatial memory and learning evaluation to further analyze performance in robots. The proposed set of metrics, learning latency and ability to navigate minimal distance to goal, should offer the robotics community additional tools to assess performance and validity of models in biologically-inspired robotic architectures at the task performance level. We also provide a comparative evaluation using these metrics between similar spatial tasks performed by rat and robot in comparable environments.