Degree Correlations Optimize Neuronal Network Sensitivity to Sub-Threshold Stimuli.
Information processing in the brain crucially depends on the topology of the neuronal connections. We investigate how the topology influences the response of a population of leaky integrate-and-fire neurons to a stimulus. We devise a method to calculate firing rates from a self-consistent system of...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2015-01-01
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| Series: | PLoS ONE |
| Online Access: | http://europepmc.org/articles/PMC4482728?pdf=render |
| _version_ | 1819156713321791488 |
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| author | Christian Schmeltzer Alexandre Hiroaki Kihara Igor Michailovitsch Sokolov Sten Rüdiger |
| author_facet | Christian Schmeltzer Alexandre Hiroaki Kihara Igor Michailovitsch Sokolov Sten Rüdiger |
| author_sort | Christian Schmeltzer |
| collection | DOAJ |
| description | Information processing in the brain crucially depends on the topology of the neuronal connections. We investigate how the topology influences the response of a population of leaky integrate-and-fire neurons to a stimulus. We devise a method to calculate firing rates from a self-consistent system of equations taking into account the degree distribution and degree correlations in the network. We show that assortative degree correlations strongly improve the sensitivity for weak stimuli and propose that such networks possess an advantage in signal processing. We moreover find that there exists an optimum in assortativity at an intermediate level leading to a maximum in input/output mutual information. |
| first_indexed | 2024-12-22T15:57:14Z |
| format | Article |
| id | doaj.art-f4ad919778204e50946e79a20bbb06be |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-22T15:57:14Z |
| publishDate | 2015-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-f4ad919778204e50946e79a20bbb06be2022-12-21T18:20:45ZengPublic Library of Science (PLoS)PLoS ONE1932-62032015-01-01106e012179410.1371/journal.pone.0121794Degree Correlations Optimize Neuronal Network Sensitivity to Sub-Threshold Stimuli.Christian SchmeltzerAlexandre Hiroaki KiharaIgor Michailovitsch SokolovSten RüdigerInformation processing in the brain crucially depends on the topology of the neuronal connections. We investigate how the topology influences the response of a population of leaky integrate-and-fire neurons to a stimulus. We devise a method to calculate firing rates from a self-consistent system of equations taking into account the degree distribution and degree correlations in the network. We show that assortative degree correlations strongly improve the sensitivity for weak stimuli and propose that such networks possess an advantage in signal processing. We moreover find that there exists an optimum in assortativity at an intermediate level leading to a maximum in input/output mutual information.http://europepmc.org/articles/PMC4482728?pdf=render |
| spellingShingle | Christian Schmeltzer Alexandre Hiroaki Kihara Igor Michailovitsch Sokolov Sten Rüdiger Degree Correlations Optimize Neuronal Network Sensitivity to Sub-Threshold Stimuli. PLoS ONE |
| title | Degree Correlations Optimize Neuronal Network Sensitivity to Sub-Threshold Stimuli. |
| title_full | Degree Correlations Optimize Neuronal Network Sensitivity to Sub-Threshold Stimuli. |
| title_fullStr | Degree Correlations Optimize Neuronal Network Sensitivity to Sub-Threshold Stimuli. |
| title_full_unstemmed | Degree Correlations Optimize Neuronal Network Sensitivity to Sub-Threshold Stimuli. |
| title_short | Degree Correlations Optimize Neuronal Network Sensitivity to Sub-Threshold Stimuli. |
| title_sort | degree correlations optimize neuronal network sensitivity to sub threshold stimuli |
| url | http://europepmc.org/articles/PMC4482728?pdf=render |
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