The transient and dynamic nature of splitter cells derive from behavior

During the past decades, hippocampal formation has undergone extensive studies, leading researchers to identify a vast collection of cells with functional properties. Several investigations, supported by carefully crafted models, have examined the origin of such cells. The most recent models hypothesize that temporal sequences underlie the observed spatial properties. We aim at investigating whether a random recurrent structure is sufficient to allow such latent sequence to appear. To do so, we simulated an agent with egocentric sensory inputs that must navigate and alternate choices at intersections. We were subsequently able to identify several splitter cells inside the model. Remarkably, when we systematically lesioned the identified splitter cells, the models behavioral performance remained intact, and new splitter cells consistently emerged through network reorganization. Furthermore, we successfully decoded position, orientation, and decision representations from the reservoir activity, even after repeated lesioning of the splitter cells. These findings suggest that the splitter cells activity is primarily task-driven and does not derive from a specific architecture nor learning. Any sufficiently recurrent network will exhibit such activity. Our results challenge the notion of functional necessity for specific neural populations and highlight the remarkable adaptability of neural networks in reorganizing computation to maintain behavioral performance.