• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利：CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法，用中文像聊天一样搜索，搜遍4000万医学文献。AI智能推荐，让科研检索更轻松。

立即免费搜索

文件翻译

保留排版，准确专业，支持PDF/Word/PPT等文件格式，支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述，25分钟生成高质量综述，智能提取关键信息，辅助科研写作。

立即免费体验

Neurofitter：一个适用于多种电生理神经元模型的参数调整软件包。

Neurofitter: a parameter tuning package for a wide range of electrophysiological neuron models.

出版信息

Front Neuroinform. 2007 Nov 2;1:1. doi: 10.3389/neuro.11.001.2007. eCollection 2007.

DOI:10.3389/neuro.11.001.2007
PMID:18974796
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC2525995/
Abstract

The increase in available computational power and the higher quality of experimental recordings have turned the tuning of neuron model parameters into a problem that can be solved by automatic global optimization algorithms. Neurofitter is a software tool that interfaces existing neural simulation software and sophisticated optimization algorithms with a new way to compute the error measure. This error measure represents how well a given parameter set is able to reproduce the experimental data. It is based on the phase-plane trajectory density method, which is insensitive to small phase differences between model and data. Neurofitter enables the effortless combination of many different time-dependent data traces into the error measure, allowing the neuroscientist to focus on what are the seminal properties of the model.We show results obtained by applying Neurofitter to a simple single compartmental model and a complex multi-compartmental Purkinje cell (PC) model. These examples show that the method is able to solve a variety of tuning problems and demonstrate details of its practical application.

摘要

可用计算能力的增加和实验记录质量的提高，使得神经元模型参数的调整成为一个可以通过自动全局优化算法解决的问题。Neurofitter 是一个软件工具，它将现有的神经模拟软件和复杂的优化算法与一种新的计算误差度量的方法结合在一起。该误差度量表示给定参数集能够再现实验数据的程度。它基于相平面轨迹密度方法，该方法对模型和数据之间的小相位差不敏感。Neurofitter 允许将许多不同的时变数据轨迹轻松地组合到误差度量中，从而使神经科学家能够专注于模型的主要特性。我们展示了将 Neurofitter 应用于简单的单室模型和复杂的多室浦肯野细胞 (PC) 模型所获得的结果。这些例子表明，该方法能够解决各种调整问题，并展示其实际应用的细节。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/11fa/2525995/c69152163006/fninf-01-001-g014.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/11fa/2525995/d784ddedad62/fninf-01-001-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/11fa/2525995/2597fecd3808/fninf-01-001-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/11fa/2525995/768b76c1044f/fninf-01-001-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/11fa/2525995/fe94d83ffdbe/fninf-01-001-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/11fa/2525995/db0b8713d846/fninf-01-001-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/11fa/2525995/e764e133b9a4/fninf-01-001-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/11fa/2525995/b4ad52ae7909/fninf-01-001-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/11fa/2525995/028c8c4ae4ad/fninf-01-001-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/11fa/2525995/4e2570ea0c13/fninf-01-001-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/11fa/2525995/03cdfbdb5be0/fninf-01-001-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/11fa/2525995/e46a8af074a6/fninf-01-001-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/11fa/2525995/6138a0c0b80a/fninf-01-001-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/11fa/2525995/99641bd8ea91/fninf-01-001-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/11fa/2525995/c69152163006/fninf-01-001-g014.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/11fa/2525995/d784ddedad62/fninf-01-001-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/11fa/2525995/2597fecd3808/fninf-01-001-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/11fa/2525995/768b76c1044f/fninf-01-001-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/11fa/2525995/fe94d83ffdbe/fninf-01-001-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/11fa/2525995/db0b8713d846/fninf-01-001-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/11fa/2525995/e764e133b9a4/fninf-01-001-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/11fa/2525995/b4ad52ae7909/fninf-01-001-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/11fa/2525995/028c8c4ae4ad/fninf-01-001-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/11fa/2525995/4e2570ea0c13/fninf-01-001-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/11fa/2525995/03cdfbdb5be0/fninf-01-001-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/11fa/2525995/e46a8af074a6/fninf-01-001-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/11fa/2525995/6138a0c0b80a/fninf-01-001-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/11fa/2525995/99641bd8ea91/fninf-01-001-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/11fa/2525995/c69152163006/fninf-01-001-g014.jpg

相似文献

1
Neurofitter: a parameter tuning package for a wide range of electrophysiological neuron models.Neurofitter：一个适用于多种电生理神经元模型的参数调整软件包。
Front Neuroinform. 2007 Nov 2;1:1. doi: 10.3389/neuro.11.001.2007. eCollection 2007.
2
Automated neuron model optimization techniques: a review.自动化神经元模型优化技术：综述
Biol Cybern. 2008 Nov;99(4-5):241-51. doi: 10.1007/s00422-008-0257-6. Epub 2008 Nov 15.
3
Automatic bi-objective parameter tuning for inverse planning of high-dose-rate prostate brachytherapy.自动双目标参数调整用于高剂量率前列腺近距离治疗的逆向计划。
Phys Med Biol. 2020 Apr 2;65(7):075009. doi: 10.1088/1361-6560/ab7362.
4
Parameter Optimization Using Covariance Matrix Adaptation-Evolutionary Strategy (CMA-ES), an Approach to Investigate Differences in Channel Properties Between Neuron Subtypes.使用协方差矩阵自适应进化策略（CMA-ES）进行参数优化，一种研究神经元亚型之间通道特性差异的方法。
Front Neuroinform. 2018 Jul 31;12:47. doi: 10.3389/fninf.2018.00047. eCollection 2018.
5
A flexible, interactive software tool for fitting the parameters of neuronal models.一种用于拟合神经元模型参数的灵活交互式软件工具。
Front Neuroinform. 2014 Jul 10;8:63. doi: 10.3389/fninf.2014.00063. eCollection 2014.
6
Multi-start Evolutionary Nonlinear OpTimizeR (MENOTR): A hybrid parameter optimization toolbox.多启动进化非线性优化器（MENOTR）：一种混合参数优化工具箱。
Biophys Chem. 2021 Dec;279:106682. doi: 10.1016/j.bpc.2021.106682. Epub 2021 Sep 29.
7
MLAGO: machine learning-aided global optimization for Michaelis constant estimation of kinetic modeling.MLAGO：用于米氏常数估计的机器学习辅助全局优化的动力学建模。
BMC Bioinformatics. 2022 Nov 1;23(1):455. doi: 10.1186/s12859-022-05009-x.
8
Automatic fitting of spiking neuron models to electrophysiological recordings.尖峰神经元模型的自动拟合到电生理记录。
Front Neuroinform. 2010 Mar 5;4:2. doi: 10.3389/neuro.11.002.2010. eCollection 2010.
9
Evolving Simple Models of Diverse Intrinsic Dynamics in Hippocampal Neuron Types.海马神经元类型中多样内在动力学的演化简单模型
Front Neuroinform. 2018 Mar 13;12:8. doi: 10.3389/fninf.2018.00008. eCollection 2018.
10
Multi-Objective Optimization Tuning Framework for Kinetic Parameter Selection and Estimation.多目标优化调整框架，用于动力学参数选择和估计。
Methods Mol Biol. 2022;2385:65-89. doi: 10.1007/978-1-0716-1767-0_4.

引用本文的文献

1
Evaluation and comparison of methods for neuronal parameter optimization using the Neuroptimus software framework.使用Neuroptimus软件框架对神经元参数优化方法进行评估与比较。
PLoS Comput Biol. 2024 Dec 23;20(12):e1012039. doi: 10.1371/journal.pcbi.1012039. eCollection 2024 Dec.
2
Multimodal parameter spaces of a complex multi-channel neuron model.一个复杂多通道神经元模型的多模态参数空间。
Front Syst Neurosci. 2022 Oct 20;16:999531. doi: 10.3389/fnsys.2022.999531. eCollection 2022.
3
Black-box and surrogate optimization for tuning spiking neural models of striatum plasticity.

本文引用的文献

1
A novel multiple objective optimization framework for constraining conductance-based neuron models by experimental data.一种通过实验数据约束基于电导的神经元模型的新型多目标优化框架。
Front Neurosci. 2007 Oct 15;1(1):7-18. doi: 10.3389/neuro.01.1.1.001.2007. eCollection 2007 Nov.
2
The action potential in mammalian central neurons.哺乳动物中枢神经元中的动作电位。
Nat Rev Neurosci. 2007 Jun;8(6):451-65. doi: 10.1038/nrn2148.
3
A parameter-space search algorithm tested on a Hodgkin-Huxley model.在霍奇金-赫胥黎模型上测试的一种参数空间搜索算法。
用于调整纹状体可塑性脉冲神经模型的黑箱和代理优化
Front Neuroinform. 2022 Oct 20;16:1017222. doi: 10.3389/fninf.2022.1017222. eCollection 2022.
4
Training spiking neuronal networks to perform motor control using reinforcement and evolutionary learning.利用强化学习和进化学习训练脉冲神经网络以执行运动控制。
Front Comput Neurosci. 2022 Sep 30;16:1017284. doi: 10.3389/fncom.2022.1017284. eCollection 2022.
5
Scaling and Benchmarking an Evolutionary Algorithm for Constructing Biophysical Neuronal Models.用于构建生物物理神经元模型的进化算法的规模评估与基准测试
Front Neuroinform. 2022 Jun 17;16:882552. doi: 10.3389/fninf.2022.882552. eCollection 2022.
6
An electrodiffusive neuron-extracellular-glia model for exploring the genesis of slow potentials in the brain.一种用于探索大脑中慢电位起源的电扩散神经元-细胞外-胶质细胞模型。
PLoS Comput Biol. 2021 Jul 16;17(7):e1008143. doi: 10.1371/journal.pcbi.1008143. eCollection 2021 Jul.
7
Xolotl: An Intuitive and Approachable Neuron and Network Simulator for Research and Teaching.Xolotl：一款用于研究和教学的直观且易用的神经元与神经网络模拟器。
Front Neuroinform. 2018 Nov 26;12:87. doi: 10.3389/fninf.2018.00087. eCollection 2018.
8
Evolving Simple Models of Diverse Intrinsic Dynamics in Hippocampal Neuron Types.海马神经元类型中多样内在动力学的演化简单模型
Front Neuroinform. 2018 Mar 13;12:8. doi: 10.3389/fninf.2018.00008. eCollection 2018.
9
Systematic generation of biophysically detailed models for diverse cortical neuron types.为多种皮层神经元类型系统地生成生物物理细节模型。
Nat Commun. 2018 Feb 19;9(1):710. doi: 10.1038/s41467-017-02718-3.
10
Optimizing computer models of corticospinal neurons to replicate in vitro dynamics.优化皮质脊髓神经元的计算机模型以复制体外动力学。
J Neurophysiol. 2017 Jan 1;117(1):148-162. doi: 10.1152/jn.00570.2016. Epub 2016 Oct 19.
Biol Cybern. 2007 Jun;96(6):625-34. doi: 10.1007/s00422-007-0156-2. Epub 2007 May 9.
4
Mapping function onto neuronal morphology.将功能映射到神经元形态上。
J Neurophysiol. 2007 Jul;98(1):513-26. doi: 10.1152/jn.00865.2006. Epub 2007 Apr 11.
5
Global neuroinformatics: the International Neuroinformatics Coordinating Facility.全球神经信息学：国际神经信息学协调设施
J Neurosci. 2007 Apr 4;27(14):3613-5. doi: 10.1523/JNEUROSCI.0558-07.2007.
6
Genetic algorithm for optimization and specification of a neuron model.
Conf Proc IEEE Eng Med Biol Soc. 2005;2005:4321-3. doi: 10.1109/IEMBS.2005.1615421.
7
Where's the beef ? Missing data in the information age.关键内容何在？信息时代的数据缺失问题。
Neuroinformatics. 2006 Winter;4(4):271-3. doi: 10.1385/NI:4:4:271.
8
The ups and downs of neuroscience shares.神经科学股票的涨跌
Neuroinformatics. 2006 Summer;4(3):213-6. doi: 10.1385/NI:4:3:213.
9
Complex parameter landscape for a complex neuron model.复杂神经元模型的复杂参数格局。
PLoS Comput Biol. 2006 Jul 21;2(7):e94. doi: 10.1371/journal.pcbi.0020094.
10
Variability, compensation and homeostasis in neuron and network function.神经元及网络功能中的变异性、代偿与内稳态
Nat Rev Neurosci. 2006 Jul;7(7):563-74. doi: 10.1038/nrn1949.