Navigation requires coordination of egocentric and allocentric spatial reference frames and may involve vectorial computations relative to landmarks. Creation of a representation of target heading relative to landmarks could be accomplished from neurons that encode the conjunction of egocentric landmark bearings with allocentric head direction. Landmark vector representations could then be created by combining these cells with distance encoding cells. Landmark vector cells have been identified in rodent hippocampus. Given remembered vectors at goal locations, it would be possible to use such cells to compute trajectories to hidden goals. To look for the first stage in this process, we assessed parietal cortical neural activity as a function of egocentric cue light location and allocentric head direction in rats running a random sequence to light locations around a circular platform. We identified cells that exhibit the predicted egocentric-by-allocentric conjunctive characteristics and anticipate orienting toward the goal.

A central feature of theories of spatial navigation involves the representation of spatial relationships between objects in complex environments. The parietal cortex has long been linked to the processing of spatial visual information and recent evidence from single unit recording in rodents suggests a role for this region in encoding egocentric and world-centered frames. The rat parietal cortex can be subdivided into up to four distinct rostral-caudal and medial-lateral regions, which includes a zone previously characterized as secondary visual cortex. At present, very little is known regarding the relative connectivity of these parietal subdivisions. Thus, we set out to map the connectivity of the entire anterior-posterior and medial-lateral span of this region. To do this we used anterograde and retrograde tracers in conjunction with open source neuronal segmentation and tracer detection tools to generate whole brain connectivity maps of parietal inputs and outputs. Our present results show that inputs to the parietal cortex varied significantly along the medial-lateral, but not the rostral-caudal axis. Specifically, retrosplenial connectivity is greater medially, but connectivity with visual cortex, though generally sparse, is more significant laterally. Finally, based on connection density, the connectivity between parietal cortex and hippocampus is indirect and likely achieved largely via dysgranular retrosplenial cortex. Thus, similar to primates, the parietal cortex of rats exhibits a difference in connectivity along the medial-lateral axis, which may represent functionally distinct areas.

Recent advances in artificial intelligence (AI) and neuroscience are impressive. In AI, this includes the development of computer programs that can beat a grandmaster at GO or outperform human radiologists at cancer detection. A great deal of these technological developments are directly related to progress in artificial neural networks—initially inspired by our knowledge about how the brain carries out computation. In parallel, neuroscience has also experienced significant advances in understanding the brain. For example, in the field of spatial navigation, knowledge about the mechanisms and brain regions involved in neural computations of cognitive maps—an internal representation of space—recently received the Nobel Prize in medicine. Much of the recent progress in neuroscience has partly been due to the development of technology used to record from very large populations of neurons in multiple regions of the brain with exquisite temporal and spatial resolution in behaving animals. With the advent of the vast quantities of data that these techniques allow us to collect there has been an increased interest in the intersection between AI and neuroscience, many of these intersections involve using AI as a novel tool to explore and analyze these large data sets. However, given the common initial motivation point—to understand the brain—these disciplines could be more strongly linked. Currently much of this potential synergy is not being realized. We propose that spatial navigation is an excellent area in which these two disciplines can converge to help advance what we know about the brain. In this review, we first summarize progress in the neuroscience of spatial navigation and reinforcement learning. We then turn our attention to discuss how spatial navigation has been modeled using descriptive, mechanistic, and normative approaches and the use of AI in such models. Next, we discuss how AI can advance neuroscience, how neuroscience can advance AI, and the limitations of these approaches. We finally conclude by highlighting promising lines of research in which spatial navigation can be the point of intersection between neuroscience and AI and how this can contribute to the advancement of the understanding of intelligent behavior.