arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2210.01767q-bio.NCcs.LGcs.NE
收藏

多个放电事件下神经元群体的有监督参数估计

Supervised Parameter Estimation of Neuron Populations from Multiple Firing Events

University of Pennsylvania（宾夕法尼亚大学）；University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校）

Long Le, Yao Li

备注：
31 pages
阅读 PDF
arXiv 页面 ↗HTML 原文 ↗

摘要：

在数学模型中，生物神经元的放电动力学通常由模型的参数决定，这些参数代表了神经元的基本属性。参数估计问题试图从单个神经元或神经元群体对外部刺激的响应以及它们之间的相互作用中恢复这些参数。在文献中解决这个问题的最常见的方法使用一些机械模型结合基于模拟或基于解决方案的优化方案。在本文中，我们研究了一种自动学习的方法，神经元种群的参数从一个训练集组成的尖峰序列和参数标签对通过监督学习。与以前的工作不同，这种自动学习不需要在推理时进行额外的模拟，也不需要在推导解析解或构建近似模型时使用专家知识。我们模拟了许多神经元群体与不同的参数设置使用随机神经元模型。使用这些数据，我们训练了各种监督机器学习模型，包括卷积和深度神经网络、随机森林和支持向量回归。然后，我们将其性能与经典方法进行比较，包括遗传搜索，贝叶斯序贯估计和随机游走近似模型。监督模型在参数估计和锋电位重建误差以及计算量方面几乎总是优于经典方法。特别是卷积神经网络，在所有度量的所有模型中是最好的。监督模型也可以在一定程度上推广到分布外的数据。

英文摘要：

The firing dynamics of biological neurons in mathematical models is often determined by the model's parameters, representing the neurons' underlying properties. The parameter estimation problem seeks to recover those parameters of a single neuron or a neuron population from their responses to external stimuli and interactions between themselves. Most common methods for tackling this problem in the literature use some mechanistic models in conjunction with either a simulation-based or solution-based optimization scheme. In this paper, we study an automatic approach of learning the parameters of neuron populations from a training set consisting of pairs of spiking series and parameter labels via supervised learning. Unlike previous work, this automatic learning does not require additional simulations at inference time nor expert knowledge in deriving an analytical solution or in constructing some approximate models. We simulate many neuronal populations with different parameter settings using a stochastic neuron model. Using that data, we train a variety of supervised machine learning models, including convolutional and deep neural networks, random forest, and support vector regression. We then compare their performance against classical approaches including a genetic search, Bayesian sequential estimation, and a random walk approximate model. The supervised models almost always outperform the classical methods in parameter estimation and spike reconstruction errors, and computation expense. Convolutional neural network, in particular, is the best among all models across all metrics. The supervised models can also generalize to out-of-distribution data to a certain extent.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

↑