Learning biological neuronal networks with artificial neural networks: neural oscillations
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2022
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866912130756771840 |
|---|---|
| author | Zhang, Ruilin Wang, Zhongyi Wu, Tianyi Cai, Yuhang Tao, Louis Xiao, Zhuo-Cheng Li, Yao |
| author_facet | Zhang, Ruilin Wang, Zhongyi Wu, Tianyi Cai, Yuhang Tao, Louis Xiao, Zhuo-Cheng Li, Yao |
| contents | First-principles-based modelings have been extremely successful in providing crucial insights and predictions for complex biological functions and phenomena. However, they can be hard to build and expensive to simulate for complex living systems. On the other hand, modern data-driven methods thrive at modeling many types of high-dimensional and noisy data. Still, the training and interpretation of these data-driven models remain challenging. Here, we combine the two types of methods to model stochastic neuronal network oscillations. Specifically, we develop a class of first-principles-based artificial neural networks to provide faithful surrogates to the high-dimensional, nonlinear oscillatory dynamics produced by neural circuits in the brain. Furthermore, when the training data set is enlarged within a range of parameter choices, the artificial neural networks become generalizable to these parameters, covering cases in distinctly different dynamical regimes. In all, our work opens a new avenue for modeling complex neuronal network dynamics with artificial neural networks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2211_11169 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Learning biological neuronal networks with artificial neural networks: neural oscillations Zhang, Ruilin Wang, Zhongyi Wu, Tianyi Cai, Yuhang Tao, Louis Xiao, Zhuo-Cheng Li, Yao Adaptation and Self-Organizing Systems Neurons and Cognition First-principles-based modelings have been extremely successful in providing crucial insights and predictions for complex biological functions and phenomena. However, they can be hard to build and expensive to simulate for complex living systems. On the other hand, modern data-driven methods thrive at modeling many types of high-dimensional and noisy data. Still, the training and interpretation of these data-driven models remain challenging. Here, we combine the two types of methods to model stochastic neuronal network oscillations. Specifically, we develop a class of first-principles-based artificial neural networks to provide faithful surrogates to the high-dimensional, nonlinear oscillatory dynamics produced by neural circuits in the brain. Furthermore, when the training data set is enlarged within a range of parameter choices, the artificial neural networks become generalizable to these parameters, covering cases in distinctly different dynamical regimes. In all, our work opens a new avenue for modeling complex neuronal network dynamics with artificial neural networks. |
| title | Learning biological neuronal networks with artificial neural networks: neural oscillations |
| topic | Adaptation and Self-Organizing Systems Neurons and Cognition |
| url | https://arxiv.org/abs/2211.11169 |