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Place cells in the hippocampus are active when an animal visits a certainlocation (referred to as a place field) within an environment. Grid cells inthe medial entorhinal cortex (MEC) respond at multiple locations, with firingfields that form a periodic and hexagonal tiling of the environment. The jointactivity of grid and place cell populations, as a function of location, forms aneural code for space. An ensemble of codes is generated by varying grid andplace cell population parameters. For each code in this ensemble, codewords aregenerated by stimulating a network with a discrete set of locations. In thismanuscript, we develop an understanding of the relationships between codingtheoretic properties of these combined populations and code constructionparameters. These relationships are revisited by measuring the performances ofbiologically realizable algorithms implemented by networks of place and gridcell populations, as well as constraint neurons, which perform de-noisingoperations. Objectives of this work include the investigation of codingtheoretic limitations of the mammalian neural code for location and howcommunication between grid and place cell networks may improve the accuracy ofeach population's representation. Simulations demonstrate that de-noisingmechanisms analyzed here can significantly improve fidelity of this neuralrepresentation of space. Further, patterns observed in connectivity of eachpopulation of simulated cells suggest thatinter-hippocampal-medial-entorhinal-cortical connectivity decreases downwardalong the dorsoventral axis.

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