The Quasi-Systematic Nature of Splitter Cells

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 model's behavioral performance remained intact: in the vast majority of cases, new splitter cells reemerged through network reorganization, while in the remaining cases, the task was solved without any detectable splitter cells, demonstrating that splitter cells are not necessary to the task resolution. Position, orientation, and decision representations could also be successfully decoded from the reservoir activity, even after repeated lesioning. Subspace alignment analysis further revealed that this reorganization preserves the task-relevant population geometry while redistributing activity within the null subspace, with the trajectory-encoding dimension rotating in neuron space across lesions. Together, these findings demonstrate that splitter cell activity is primarily task driven and does not derive from a specific architecture or learning rule: splitter cells emerge generically across random recurrent networks that successfully solve the task, across a broad and robust range of dynamical parameters and are not necessary for task performance. Our results therefore challenge the notion of functional necessity for specific neural populations.