On the organization of grid and place cells: Neural de-noising via subspace learning

TL;DR

This paper explores how grid and place cells in the hippocampus and entorhinal cortex encode spatial information, using coding theory and neural de-noising algorithms to improve the accuracy of neural spatial representations.

Contribution

It introduces a framework linking coding theoretic properties with neural network parameters and demonstrates how biologically plausible de-noising algorithms enhance spatial coding fidelity.

Findings

De-noising mechanisms significantly improve neural spatial representation accuracy.

Connectivity patterns suggest a dorsoventral decrease in inter-hippocampal-entorhinal interactions.

Coding limitations of the neural spatial code are characterized and analyzed.

Abstract

Place cells in the hippocampus are active when an animal visits a certain location (referred to as a place field) within an environment. Grid cells in the medial entorhinal cortex (MEC) respond at multiple locations, with firing fields that form a periodic and hexagonal tiling of the environment. The joint activity of grid and place cell populations, as a function of location, forms a neural code for space. An ensemble of codes is generated by varying grid and place cell population parameters. For each code in this ensemble, codewords are generated by stimulating a network with a discrete set of locations. In this manuscript, we develop an understanding of the relationships between coding theoretic properties of these combined populations and code construction parameters. These relationships are revisited by measuring the performances of biologically realizable algorithms implemented by networks of place and grid cell populations, as well as constraint neurons, which perform de-noising operations. Objectives of this work include the investigation of coding theoretic limitations of the mammalian neural code for location and how communication between grid and place cell networks may improve the accuracy of each population's representation. Simulations demonstrate that de-noising mechanisms analyzed here can significantly improve fidelity of this neural representation of space. Further, patterns observed in connectivity of each population of simulated cells suggest that inter-hippocampal-medial-entorhinal-cortical connectivity decreases downward along the dorsoventral axis.