CRII: RI: Navigational Circuitry of Brain: Novel Neural Codes with Diversity for Robust and Adaptive Location Processing

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The main objectives of this project are to develop a novel coding framework for the navigational circuitry of the brain, and in the context of this model, to investigate how neural diversity and neurally plausible information processing mechanisms contribute to a robust navigation system. Robust localization techniques based on neural coding principles may have applications in robotics. In addition, the framework developed here will lead to a better understanding of brain's navigational circuitry and associated brain areas, which are implicated in Alzheimer's disease and other brain disorders.The brain's navigational circuitry includes cells whose activities are modulated depending on where an animal is in its environment. Place cells in the hippocampus are typically active over a certain region within an environment, but remain mostly silent otherwise. Grid cells in the entorhinal cortex, on the other hand, respond at multiple regions, where the activity of each cell follows a striking periodic pattern, forming a hexagonal tiling of the space. Both place and grid populations exhibit diversity (e.g., firing fields of neurons typically increase along the dorsoventral axis) and adaptivity (e.g., cells change their activity levels in response to changes in environment). One theory of how these cells are related is that grid cells form place cells, which in turn are responsible for performing navigational computations, but this view contradicts recent neurophysiological data. The central hypothesis of the investigator, in contrast, is that the brain has a neural code consisting of both place cells and grid cells, and, when processed with neurally plausible, low-complexity algorithms, codewords of this code form a robust and adaptive navigational processing mechanism. This project focuses on coding models consisting of place cells that provide side information in decoding of the grid cell activity, and separate read-out populations that perform computations with place and grid cells iteratively. Utilizing these models, the investigator proposes to study the role of neural diversity in achieving robustness, to analyze the modular processing capacity of the code, to understand neural adaptivity through obtaining broader estimate by lowering the code rate, and to compare these findings with experimental observations.