Localized Space Coding and Phase Coding Complement Each Other to Achieve Robust and Efficient Spatial Representation

Abstract

Localized space coding and phase coding are two distinct strategies responsible, respectively, for representing abstract structure and sensory observations in neural cognitive maps. In spatial representation, localized space coding is implemented by place cells in the hippocampus (HPC), while phase coding is implemented by grid cells in the medial entorhinal cortex (MEC). Both strategies have their own advantages and disadvantages, and neither of them meets the requirement of representing space robustly and efficiently in the brain. Here, we show that through reciprocal connections between HPC and MEC, place and grid cells can complement each other to overcome their respective shortcomings. Specifically, we build a coupled network model, in which a continuous attractor neural network (CANN) with position coordinate models place cells, while multiple CANNs with phase coordinates model grid cell modules with varying spacings. The reciprocal connections between place and grid cells encode the correlation prior between the sensory cues processed by HPC and MEC, respectively. Using this model, we show that: 1) place and grid cells interact to integrate sensory cues in a Bayesian manner; 2) place cells complement grid cells in coding accuracy by eliminating non-local errors of the latter; 3) grid cells complement place cells in coding efficiency by enlarging the number of environmental maps stored stably by the latter. We demonstrate that the coupled network model explains the seemingly contradictory experimental findings about the remapping phenomena of place cells when grid cells are either inactivated or depolarized. This study gives us insight into understanding how the brain employs collaborative localized and phase coding to realize both robust and efficient information representation.