Modeling place cells and grid cells in multi-compartment environments: Entorhinal-hippocampal loop as a multisensory integration circuit

Abstract

Hippocampal place cells and entorhinal grid cells are thought to form a representation of space by integrating internal and external sensory cues. Experimental data show that different subsets of place cells are controlled by vision, self-motion or a combination of both. Moreover, recent studies in environments with a high degree of visual aliasing suggest that a continuous interaction between place cells and grid cells can result in a deformation of hexagonal grids or in a progressive loss of visual cue control over grid fields. The computational nature of such a bidirectional interaction remains unclear. In this work we present a neural network model of the dynamic interaction between place cells and grid cells within the entorhinal-hippocampal processing loop. The model was tested in two recent experimental paradigms involving environments with visually similar compartments that provided conflicting evidence about visual cue control over self-motion-based spatial codes. Analysis of the model behavior suggests that the strength of entorhinal-hippocampal dynamical loop is the key parameter governing differential cue control in multi-compartment environments. Moreover, construction of separate spatial representations of visually identical compartments required a progressive weakening of visual cue control over place fields in favor of self-motion based mechanisms. More generally our results suggest a functional segregation between plastic and dynamic processes in hippocampal processing.

Keywords: Computational model; Grid cells; Hippocampus; Multisensory integration; Neural network; Place cells.