Name
Abstract | ||
|---|---|---|
Author summary Neurons form complex networks that play a critical role in the development and aging of the brain, as well as in health and disease. Understanding how these networks form and evolve over time can lead us to advances in neuronal and cognitive health. Previous studies have mainly used summary statistics or graph features recorded at different points in time to analyze neuronal networks. However, this approach ignores the temporal dependency of these features and may lead to discovering spurious patterns in the data. In order to thoroughly characterize neuronal networks, time dependencies must be explicitly modeled. We present a statistical model that captures both the underlying structural and temporal dynamics of neuronal networks. 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. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1371/journal.pcbi.1007834 | PLOS COMPUTATIONAL BIOLOGY |
DocType | Volume | Issue |
Journal | 16 | 5 |
ISSN | Citations | PageRank |
1553-734X | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jose Cadena | 1 | 98 | 7.53 |
| Ana Paula Sales | 2 | 0 | 0.34 |
| Doris Lam | 3 | 0 | 0.34 |
| Heather A Enright | 4 | 0 | 0.34 |
| Elizabeth K. Wheeler | 5 | 0 | 0.68 |
| Nicholas O Fischer | 6 | 0 | 0.34 |