This tutorial illustrates the use of data assimilation algorithms to estimate unobserved variables and unknown parameters of conductance-based neuronal models. Modern data assimilation (DA) techniques are widely used in climate science and weather prediction, but have only recently begun to be applied in neuroscience. The two main classes of DA techniques are sequential methods and variational methods. We provide computer code implementing basic versions of a method from each class, the Unscented Kalman Filter and 4D-Var, and demonstrate how to use these algorithms to infer several parameters of the Morris–Lecar model from a single voltage trace. Depending on parameters, the Morris–Lecar model exhibits qualitatively different types of neuronal excitability due to changes in the underlying bifurcation structure. We show that when presented with voltage traces from each of the various excitability regimes, the DA methods can identify parameter sets that produce the correct bifurcation structure even with initial parameter guesses that correspond to a different excitability regime. This demonstrates the ability of DA techniques to perform nonlinear state and parameter estimation and introduces the geometric structure of inferred models as a novel qualitative measure of estimation success. We conclude by discussing extensions of these DA algorithms that have appeared in the neuroscience literature.

Matthew J. Moye

Casey O. Diekman

The Journal of Mathematical Neuroscience

数学神经科学杂志 (JMN) 发表关于神经科学所有领域的数学建模和分析的研究文章，即神经系统及其功能障碍的研究。重点是使用数学作为主要工具来阐明负责在所有相关尺度（从分子世界到认知世界）的神经科学中实验观察到的行为的基本机制。其目的是发表使用先进数学技术来阐明这些问题的作品。它发表完整的原创论文、快速交流和评论文章。特别鼓励结合令人信服的数值实验支持的理论结果的论文。也欢迎介绍和帮助开发可能与未来神经系统特别是人类大脑研究相关的新数学理论的论文.

The Journal of Mathematical Neuroscience (JMN) is dedicated to publishing research articles on mathematical modeling and analysis across all fields of neuroscience, focusing on the study of the nervous system and its disorders. The journal emphasizes the use of mathematics as a primary tool to elucidate the fundamental mechanisms behind experimentally observed behaviors in neuroscience, spanning from the molecular to the cognitive level. We aim to publish original research papers, rapid communications, and review articles that employ advanced mathematical techniques to address these issues. We particularly encourage submissions that present theoretical results supported by compelling numerical experiments. Additionally, we welcome papers that introduce and help develop new mathematical theories that may be relevant to future research on the nervous system, especially the human brain.

《Journal of Mathematical Neuroscience》是一本专注于神经科学各个领域的数学建模与分析研究的期刊。我们致力于使用数学工具揭示神经系统及其功能障碍的基本机制，涵盖从分子层面到认知层面的实验观察。期刊欢迎发表使用先进数学技术的原创研究论文、快速交流和评论文章，特别鼓励那些结合有力数值实验支持的理论成果的研究。同时，我们也欢迎介绍和发展与未来神经系统研究，尤其是人类大脑相关的新数学理论的论文。

Category	Quartile
医学	4
医学, 数学与计算生物学	3
医学, 数学跨学科应用	3
医学, 神经科学	4

Data Assimilation Methods for Neuronal State and Parameter Estimation

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