Learning biological neuronal networks with artificial neural networks: neural oscillations

TL;DR

This paper introduces a hybrid modeling approach combining first-principles and artificial neural networks to accurately simulate complex neuronal oscillations, enabling generalization across different dynamical regimes.

Contribution

It develops a new class of neural network models that faithfully replicate high-dimensional neuronal oscillations and generalize across various parameters, bridging physics-based and data-driven methods.

Findings

Neural networks accurately model stochastic neuronal oscillations.

Models generalize well across different parameter regimes.

The approach offers a new avenue for complex neuronal dynamics modeling.

Abstract

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.