Learning biological neuronal networks with artificial neural networks: neural oscillations

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...

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Bibliographic Details
Published in:arXiv.org 2022-11
Main Authors: Zhang, Ruilin, Wang, Zhongyi, Wu, Tianyi, Cai, Yuhang, Tao, Louis, Zhuo-Cheng, Xiao, Yao, Li
Format: Article
Language:English
Subjects:
Artificial neural networks
First principles
Mathematical models
Modelling
Neural networks
Nonlinear dynamics
Oscillations
Parameters
Training
Online Access:Get full text
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Summary: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.
ISSN:2331-8422
DOI:10.48550/arxiv.2211.11169