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A computational model of pattern separation efficiency in the dentate gyrus with implications in schizophrenia
Information processing in the hippocampus begins by transferring spiking activity of the entorhinal cortex (EC) into the dentate gyrus (DG). Activity pattern in the EC is separated by the DG such that it plays an important role in hippocampal functions including memory. The structural and physiologi...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2015
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4373261/ https://www.ncbi.nlm.nih.gov/pubmed/25859189 http://dx.doi.org/10.3389/fnsys.2015.00042 |
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author | Faghihi, Faramarz Moustafa, Ahmed A. |
author_facet | Faghihi, Faramarz Moustafa, Ahmed A. |
author_sort | Faghihi, Faramarz |
collection | PubMed |
description | Information processing in the hippocampus begins by transferring spiking activity of the entorhinal cortex (EC) into the dentate gyrus (DG). Activity pattern in the EC is separated by the DG such that it plays an important role in hippocampal functions including memory. The structural and physiological parameters of these neural networks enable the hippocampus to be efficient in encoding a large number of inputs that animals receive and process in their life time. The neural encoding capacity of the DG depends on its single neurons encoding and pattern separation efficiency. In this study, encoding by the DG is modeled such that single neurons and pattern separation efficiency are measured using simulations of different parameter values. For this purpose, a probabilistic model of single neurons efficiency is presented to study the role of structural and physiological parameters. Known neurons number of the EC and the DG is used to construct a neural network by electrophysiological features of granule cells of the DG. Separated inputs as activated neurons in the EC with different firing probabilities are presented into the DG. For different connectivity rates between the EC and DG, pattern separation efficiency of the DG is measured. The results show that in the absence of feedback inhibition on the DG neurons, the DG demonstrates low separation efficiency and high firing frequency. Feedback inhibition can increase separation efficiency while resulting in very low single neuron’s encoding efficiency in the DG and very low firing frequency of neurons in the DG (sparse spiking). This work presents a mechanistic explanation for experimental observations in the hippocampus, in combination with theoretical measures. Moreover, the model predicts a critical role for impaired inhibitory neurons in schizophrenia where deficiency in pattern separation of the DG has been observed. |
format | Online Article Text |
id | pubmed-4373261 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-43732612015-04-09 A computational model of pattern separation efficiency in the dentate gyrus with implications in schizophrenia Faghihi, Faramarz Moustafa, Ahmed A. Front Syst Neurosci Neuroscience Information processing in the hippocampus begins by transferring spiking activity of the entorhinal cortex (EC) into the dentate gyrus (DG). Activity pattern in the EC is separated by the DG such that it plays an important role in hippocampal functions including memory. The structural and physiological parameters of these neural networks enable the hippocampus to be efficient in encoding a large number of inputs that animals receive and process in their life time. The neural encoding capacity of the DG depends on its single neurons encoding and pattern separation efficiency. In this study, encoding by the DG is modeled such that single neurons and pattern separation efficiency are measured using simulations of different parameter values. For this purpose, a probabilistic model of single neurons efficiency is presented to study the role of structural and physiological parameters. Known neurons number of the EC and the DG is used to construct a neural network by electrophysiological features of granule cells of the DG. Separated inputs as activated neurons in the EC with different firing probabilities are presented into the DG. For different connectivity rates between the EC and DG, pattern separation efficiency of the DG is measured. The results show that in the absence of feedback inhibition on the DG neurons, the DG demonstrates low separation efficiency and high firing frequency. Feedback inhibition can increase separation efficiency while resulting in very low single neuron’s encoding efficiency in the DG and very low firing frequency of neurons in the DG (sparse spiking). This work presents a mechanistic explanation for experimental observations in the hippocampus, in combination with theoretical measures. Moreover, the model predicts a critical role for impaired inhibitory neurons in schizophrenia where deficiency in pattern separation of the DG has been observed. Frontiers Media S.A. 2015-03-25 /pmc/articles/PMC4373261/ /pubmed/25859189 http://dx.doi.org/10.3389/fnsys.2015.00042 Text en Copyright © 2015 Faghihi and Moustafa. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neuroscience Faghihi, Faramarz Moustafa, Ahmed A. A computational model of pattern separation efficiency in the dentate gyrus with implications in schizophrenia |
title | A computational model of pattern separation efficiency in the dentate gyrus with implications in schizophrenia |
title_full | A computational model of pattern separation efficiency in the dentate gyrus with implications in schizophrenia |
title_fullStr | A computational model of pattern separation efficiency in the dentate gyrus with implications in schizophrenia |
title_full_unstemmed | A computational model of pattern separation efficiency in the dentate gyrus with implications in schizophrenia |
title_short | A computational model of pattern separation efficiency in the dentate gyrus with implications in schizophrenia |
title_sort | computational model of pattern separation efficiency in the dentate gyrus with implications in schizophrenia |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4373261/ https://www.ncbi.nlm.nih.gov/pubmed/25859189 http://dx.doi.org/10.3389/fnsys.2015.00042 |
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