Modeling the temporal network dynamics of neuronal cultures.
Neurons form complex networks that evolve over multiple time scales. In order to thoroughly characterize these networks, time dependencies must be explicitly modeled. Here, we present a statistical model that captures both the underlying structural and temporal dynamics of neuronal networks. Our mod...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2020-05-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.1007834 |
| _version_ | 1819008093477928960 |
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| author | Jose Cadena Ana Paula Sales Doris Lam Heather A Enright Elizabeth K Wheeler Nicholas O Fischer |
| author_facet | Jose Cadena Ana Paula Sales Doris Lam Heather A Enright Elizabeth K Wheeler Nicholas O Fischer |
| author_sort | Jose Cadena |
| collection | DOAJ |
| description | Neurons form complex networks that evolve over multiple time scales. In order to thoroughly characterize these networks, time dependencies must be explicitly modeled. Here, we present a statistical model that captures both the underlying structural and temporal dynamics of neuronal networks. Our model combines the class of Stochastic Block Models for community formation with Gaussian processes to model changes in the community structure as a smooth function of time. We validate our model on synthetic data and demonstrate its utility on three different studies using in vitro cultures of dissociated neurons. |
| first_indexed | 2024-12-21T00:35:00Z |
| format | Article |
| id | doaj.art-f359b178d0224a089a3eb228b7c4b7e6 |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-12-21T00:35:00Z |
| publishDate | 2020-05-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-f359b178d0224a089a3eb228b7c4b7e62022-12-21T19:21:49ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582020-05-01165e100783410.1371/journal.pcbi.1007834Modeling the temporal network dynamics of neuronal cultures.Jose CadenaAna Paula SalesDoris LamHeather A EnrightElizabeth K WheelerNicholas O FischerNeurons form complex networks that evolve over multiple time scales. In order to thoroughly characterize these networks, time dependencies must be explicitly modeled. Here, we present a statistical model that captures both the underlying structural and temporal dynamics of neuronal networks. Our model combines the class of Stochastic Block Models for community formation with Gaussian processes to model changes in the community structure as a smooth function of time. We validate our model on synthetic data and demonstrate its utility on three different studies using in vitro cultures of dissociated neurons.https://doi.org/10.1371/journal.pcbi.1007834 |
| spellingShingle | Jose Cadena Ana Paula Sales Doris Lam Heather A Enright Elizabeth K Wheeler Nicholas O Fischer Modeling the temporal network dynamics of neuronal cultures. PLoS Computational Biology |
| title | Modeling the temporal network dynamics of neuronal cultures. |
| title_full | Modeling the temporal network dynamics of neuronal cultures. |
| title_fullStr | Modeling the temporal network dynamics of neuronal cultures. |
| title_full_unstemmed | Modeling the temporal network dynamics of neuronal cultures. |
| title_short | Modeling the temporal network dynamics of neuronal cultures. |
| title_sort | modeling the temporal network dynamics of neuronal cultures |
| url | https://doi.org/10.1371/journal.pcbi.1007834 |
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