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Title: A combinatorial model for dentate gyrus sparse coding

Abstract

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.

Authors:
 [1];  [1];  [1];  [1]
  1. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Publication Date:
Research Org.:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
1371475
Report Number(s):
SAND-2016-7981J
Journal ID: ISSN 0899-7667; 655049
Grant/Contract Number:  
AC04-94AL85000
Resource Type:
Accepted Manuscript
Journal Name:
Neural Computation
Additional Journal Information:
Journal Volume: 29; Journal Issue: 1; Journal ID: ISSN 0899-7667
Publisher:
MIT Press
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; 60 APPLIED LIFE SCIENCES; dentate gyrus; sparse coding; pattern separation; adult neurogenesis

Citation Formats

Severa, William, Parekh, Ojas, James, Conrad D., and Aimone, James B. A combinatorial model for dentate gyrus sparse coding. United States: N. p., 2016. Web. doi:10.1162/NECO_a_00905.
Severa, William, Parekh, Ojas, James, Conrad D., & Aimone, James B. A combinatorial model for dentate gyrus sparse coding. United States. https://doi.org/10.1162/NECO_a_00905
Severa, William, Parekh, Ojas, James, Conrad D., and Aimone, James B. Thu . "A combinatorial model for dentate gyrus sparse coding". United States. https://doi.org/10.1162/NECO_a_00905. https://www.osti.gov/servlets/purl/1371475.
@article{osti_1371475,
title = {A combinatorial model for dentate gyrus sparse coding},
author = {Severa, William and Parekh, Ojas and James, Conrad D. and Aimone, James B.},
abstractNote = {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.},
doi = {10.1162/NECO_a_00905},
journal = {Neural Computation},
number = 1,
volume = 29,
place = {United States},
year = {Thu Dec 29 00:00:00 EST 2016},
month = {Thu Dec 29 00:00:00 EST 2016}
}

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