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Dentate Gyrus Circuitry Features Improve Performance of Sparse Approximation Algorithms

Memory-related activity in the Dentate Gyrus (DG) is characterized by sparsity. Memory representations are seen as activated neuronal populations of granule cells, the main encoding cells in DG, which are estimated to engage 2–4% of the total population. This sparsity is assumed to enhance the abili...

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Detalles Bibliográficos
Autores principales: Petrantonakis, Panagiotis C., Poirazi, Panayiota
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4312091/
https://www.ncbi.nlm.nih.gov/pubmed/25635776
http://dx.doi.org/10.1371/journal.pone.0117023
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author Petrantonakis, Panagiotis C.
Poirazi, Panayiota
author_facet Petrantonakis, Panagiotis C.
Poirazi, Panayiota
author_sort Petrantonakis, Panagiotis C.
collection PubMed
description Memory-related activity in the Dentate Gyrus (DG) is characterized by sparsity. Memory representations are seen as activated neuronal populations of granule cells, the main encoding cells in DG, which are estimated to engage 2–4% of the total population. This sparsity is assumed to enhance the ability of DG to perform pattern separation, one of the most valuable contributions of DG during memory formation. In this work, we investigate how features of the DG such as its excitatory and inhibitory connectivity diagram can be used to develop theoretical algorithms performing Sparse Approximation, a widely used strategy in the Signal Processing field. Sparse approximation stands for the algorithmic identification of few components from a dictionary that approximate a certain signal. The ability of DG to achieve pattern separation by sparsifing its representations is exploited here to improve the performance of the state of the art sparse approximation algorithm “Iterative Soft Thresholding” (IST) by adding new algorithmic features inspired by the DG circuitry. Lateral inhibition of granule cells, either direct or indirect, via mossy cells, is shown to enhance the performance of the IST. Apart from revealing the potential of DG-inspired theoretical algorithms, this work presents new insights regarding the function of particular cell types in the pattern separation task of the DG.
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spelling pubmed-43120912015-02-13 Dentate Gyrus Circuitry Features Improve Performance of Sparse Approximation Algorithms Petrantonakis, Panagiotis C. Poirazi, Panayiota PLoS One Research Article Memory-related activity in the Dentate Gyrus (DG) is characterized by sparsity. Memory representations are seen as activated neuronal populations of granule cells, the main encoding cells in DG, which are estimated to engage 2–4% of the total population. This sparsity is assumed to enhance the ability of DG to perform pattern separation, one of the most valuable contributions of DG during memory formation. In this work, we investigate how features of the DG such as its excitatory and inhibitory connectivity diagram can be used to develop theoretical algorithms performing Sparse Approximation, a widely used strategy in the Signal Processing field. Sparse approximation stands for the algorithmic identification of few components from a dictionary that approximate a certain signal. The ability of DG to achieve pattern separation by sparsifing its representations is exploited here to improve the performance of the state of the art sparse approximation algorithm “Iterative Soft Thresholding” (IST) by adding new algorithmic features inspired by the DG circuitry. Lateral inhibition of granule cells, either direct or indirect, via mossy cells, is shown to enhance the performance of the IST. Apart from revealing the potential of DG-inspired theoretical algorithms, this work presents new insights regarding the function of particular cell types in the pattern separation task of the DG. Public Library of Science 2015-01-30 /pmc/articles/PMC4312091/ /pubmed/25635776 http://dx.doi.org/10.1371/journal.pone.0117023 Text en © 2015 Petrantonakis, Poirazi http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Petrantonakis, Panagiotis C.
Poirazi, Panayiota
Dentate Gyrus Circuitry Features Improve Performance of Sparse Approximation Algorithms
title Dentate Gyrus Circuitry Features Improve Performance of Sparse Approximation Algorithms
title_full Dentate Gyrus Circuitry Features Improve Performance of Sparse Approximation Algorithms
title_fullStr Dentate Gyrus Circuitry Features Improve Performance of Sparse Approximation Algorithms
title_full_unstemmed Dentate Gyrus Circuitry Features Improve Performance of Sparse Approximation Algorithms
title_short Dentate Gyrus Circuitry Features Improve Performance of Sparse Approximation Algorithms
title_sort dentate gyrus circuitry features improve performance of sparse approximation algorithms
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4312091/
https://www.ncbi.nlm.nih.gov/pubmed/25635776
http://dx.doi.org/10.1371/journal.pone.0117023
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