Optimal control methods for nonlinear parameter estimation in biophysical neuron models.
Optimal control methods for nonlinear parameter estimation in biophysical neuron models.
复制标题
生物物理神经元模型非线性参数估计的最优控制方法。
DOI：
10.1371/journal.pcbi.1010479
复制
发表时间：
2022-09
影响因子：
4.3
通讯作者：
中科院分区：
文献类型：
--
作者：
生物物理学上真实的神经元模型的功能形式受到神经生物学和解剖学考虑的约束，例如细胞形态和已知离子通道的存在。尽管有这些限制，神经元模型仍然包含未知的静态参数，必须从实验中推断。这个推理任务是最容易铸造成状态空间模型，系统地考虑到部分可观测性和测量噪声的框架。仅推断动态状态变量，如膜电压是一个研究得很好的问题，并已接近了广泛的技术开始与著名的卡尔曼滤波器。另一方面，同时推断状态和固定参数就不那么简单了。在这里，我们开发了一种联合参数和状态推断的方法，结合传统的状态空间建模与混沌同步和最优控制。我们的方法是专门针对具有相当大的测量噪声，稀疏的可观测性，非常非线性或混沌动力学，以及高度不知情的先验的情况。我们说明了我们的方法，在一个典型的混沌模型和现象学神经元模型，表明许多未知参数可以被发现可靠和准确地从短和嘈杂的观察到的时间轨迹。考虑到钙报告基因和遗传编码电压指标的不断改进，我们的方法有望在更大规模的系统中进行估计。系统神经科学旨在了解个体神经元和神经网络如何将外部刺激处理为行为反应。这一特征的基础是由实验观察密切塑造的数学模型。但是神经系统是高维的，包含高度非线性的相互作用，因此考虑到目前的实验能力，开发精确的模型仍然是一个挑战。在实践中，这意味着表征神经活动的动力学方程具有许多未知参数，并且这些参数必须从数据中推断。由于模型的非线性、系统和测量噪声以及电极记录的观测值的稀疏性，这个推断问题是不平凡的。在这里，我们提出了一种新的方法来推断神经系统的模型参数。我们的技术结合了控制理论和优化的思想，相当于使用数据来“控制”估计，以达到最佳拟合。我们的方法相比，以及对其他国家的最先进的推理方法，无论是在现象学混沌系统和生物物理神经元模型的准确性。我们的工作表明，许多未知的模型参数的兴趣，可以推断出电压测量，尽管信号噪声，仪器噪声，和低的可观测性。
Functional forms of biophysically-realistic neuron models are constrained by neurobiological and anatomical considerations, such as cell morphologies and the presence of known ion channels. Despite these constraints, neuron models still contain unknown static parameters which must be inferred from experiment. This inference task is most readily cast into the framework of state-space models, which systematically takes into account partial observability and measurement noise. Inferring only dynamical state variables such as membrane voltages is a well-studied problem, and has been approached with a wide range of techniques beginning with the well-known Kalman filter. Inferring both states and fixed parameters, on the other hand, is less straightforward. Here, we develop a method for joint parameter and state inference that combines traditional state space modeling with chaotic synchronization and optimal control. Our methods are tailored particularly to situations with considerable measurement noise, sparse observability, very nonlinear or chaotic dynamics, and highly uninformed priors. We illustrate our approach both in a canonical chaotic model and in a phenomenological neuron model, showing that many unknown parameters can be uncovered reliably and accurately from short and noisy observed time traces. Our method holds promise for estimation in larger-scale systems, given ongoing improvements in calcium reporters and genetically-encoded voltage indicators. Systems neuroscience aims to understand how individual neurons and neural networks process external stimuli into behavioral responses. Underlying this characterization are mathematical models intimately shaped by experimental observations. But neural systems are high-dimensional and contain highly nonlinear interactions, so developing accurate models remains a challenge given current experimental capabilities. In practice, this means that the dynamical equations characterizing neural activity have many unknown parameters, and these parameters must be inferred from data. This inference problem is nontrivial owing to model nonlinearity, system and measurement noise, and the sparsity of observations from electrode recordings. Here, we present a novel method for inferring model parameters of neural systems. Our technique combines ideas from control theory and optimization, and amounts to using data to “control” estimates toward the best fit. Our method compares well in accuracy against other state-of-the-art inference methods, both in phenomenological chaotic systems and biophysical neuron models. Our work shows that many unknown model parameters of interest can be inferred from voltage measurements, despite signaling noise, instrument noise, and low observability.
登录
查看更多内容
影响因子：
1.9
作者：
Druckmann, Shaul;Berger, Thomas K.;Segev, Idan
通讯作者：
Segev, Idan
影响因子：
2.2
作者：
Doucet, A;Godsill, S;Andrieu, C
通讯作者：
Andrieu, C
影响因子：
2.5
作者：
Brockwell, AE;Rojas, AL;Kass, RE
通讯作者：
Kass, RE
影响因子：
2.5
作者：
Daou, Arij;Ross, Matthew T.;Bertram, Richard
通讯作者：
Bertram, Richard
影响因子：
1.9
作者：
Kadakia N;Armstrong E;Breen D;Morone U;Daou A;Margoliash D;Abarbanel HDI
通讯作者：
Abarbanel HDI