Spontaneous dynamics of hippocampal place fields in a model of combinatorial competition among stable inputs

Abstract

AbstractWe present computer simulations illustrating how the plastic integration of spatially stable inputs could contribute to the dynamic character of hippocampal spatial representations. In novel environments of slightly larger size than typical apparatus, the emergence of well-defined place fields in real place cells seems to rely on inputs from normally functioning grid cells. Theoretically, the grid-to-place transformation is possible if a place cell is able to respond selectively to a combination of suitably aligned grids. We previously identified the functional characteristics that allow a synaptic plasticity rule to accomplish this selection by synaptic competition during rat foraging behavior. Here, we show that the synaptic competition can outlast the formation of place fields, contributing to their spatial reorganization over time, when the model is run in larger environments and the topographical/modular organization of grid inputs is taken into account. Co-simulated cells that differ only by their randomly assigned grid inputs display different degrees and kinds of spatial reorganization—ranging from place-field remapping to more subtle in-field changes or lapses in firing. The model predicts a greater number of place fields and propensity for remapping in place cells recorded from more septal regions of the hippocampus and/or in larger environments, motivating future experimental standardization across studies and animal models. In sum, spontaneous remapping could arise from rapid synaptic learning involving inputs that are functionally homogeneous, spatially stable, and minimally stochastic.Significance StatementIn both AI and theoretical neuroscience, learning systems often rely on the asymptotic convergence of slow-acting learning rules applied to input spaces that are presumed to be sampled repeatedly, for example over developmental timescales. Place cells of the hippocampus testify to a neural system capable of rapidly encoding cognitive variables—such as the animal’s position in space—from limited experience. These internal representations undergo “spontaneous” changes over time, spurring much interest in their cognitive significance and underlying mechanisms. We investigate a model suggesting that some of these changes could be a tradeoff of rapid learning.