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Title: A combinatorial model for dentate gyrus sparse coding

The dentate gyrus forms a critical link between the entorhinal cortex and CA3 by providing a sparse version of the signal. Concurrent with this increase in sparsity, a widely accepted theory suggests the dentate gyrus performs pattern separation—similar inputs yield decorrelated outputs. Although an active region of study and theory, few logically rigorous arguments detail the dentate gyrus’s (DG) coding. We suggest a theoretically tractable, combinatorial model for this action. The model provides formal methods for a highly redundant, arbitrarily sparse, and decorrelated output signal.To explore the value of this model framework, we assess how suitable it is for two notable aspects of DG coding: how it can handle the highly structured grid cell representation in the input entorhinal cortex region and the presence of adult neurogenesis, which has been proposed to produce a heterogeneous code in the DG. We find tailoring the model to grid cell input yields expansion parameters consistent with the literature. In addition, the heterogeneous coding reflects activity gradation observed experimentally. Lastly, we connect this approach with more conventional binary threshold neural circuit models via a formal embedding.
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  1. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Publication Date:
Report Number(s):
Journal ID: ISSN 0899-7667; 655049
Grant/Contract Number:
Accepted Manuscript
Journal Name:
Neural Computation
Additional Journal Information:
Journal Volume: 29; Journal Issue: 1; Journal ID: ISSN 0899-7667
MIT Press
Research Org:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Org:
USDOE National Nuclear Security Administration (NNSA)
Country of Publication:
United States
97 MATHEMATICS AND COMPUTING; 60 APPLIED LIFE SCIENCES; dentate gyrus; sparse coding; pattern separation; adult neurogenesis
OSTI Identifier: