The apparently simple task of navigating from one point to another depends on the coordinated deployment of multiple relatively complex and general cognitive capacities. These include operations such as pattern recognition, usage of knowledge about temporal relationships, or behavioral planning and control. However, spatial behavior is relatively simple to test, and the underlying neural responses, despite being far from the sensorimotor periphery, are often surprisingly easy to map to meaningful variables. This has led to the discovery of numerous cell types coding for different aspects of spatial behavior, making spatial navigation an ideal model for the study of the neural mechanisms responsible for higher level cognitive functions. Among the most studied spatially modulated cell types are place cells and grid cells, which signal an animal’s location by becoming active only in certain regions of the environment. Intriguingly, these cells display a form of phase coding: firing at different phases of the theta oscillation seems to represent the positions that were or will be reached by the animal in the recent past or near future, respectively. We study how networks of such phase coding cells can support spatial navigation, including functions such as path integration, prediction of future positions or movement planning. To do so, we combine computational modeling and simulation work with the analysis of experimental data.
From grid cells to place cells with realistic field sizes