下オリーブ核神経細胞群におけるギャップ結合および抑制性結合のベイズ推定

下橄榄核神经元群间隙连接和抑制连接的贝叶斯估计

• 批准号: 14J09356
• 负责人: HOANG HUUTHIEN
• 金额: $ 1.09万
• 依托单位: Ritsumeikan University
• 依托单位国家: 日本
• 项目类别: Grant-in-Aid for JSPS Fellows
• 财政年份: 2014
• 资助国家: 日本
• 起止时间: 2014-04-25 至 2016-03-31
• 项目状态: 已结题
• 来源: https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-14J09356/
• 关键词: Bayes inference; spike trains; neuron network model; Inferior olive neurons

The primary achievement of our past research was a successful construction of a sophisticated framework to resolve the problem of estimating gap-junctional and inhibitory conductance from spike trains of inferior olive neurons (Hoang et al., 2015). Traditionally, the problem to estimate model parameters of a network of neurons based on spike timings of a number of neurons is severely ill-posed. That inverse problem thus needs a stochastic approach which finds most likely solution among many possible ones. We developed a theoretical framework of Bayesian estimation, which allows segmental fluctuations of parameter estimates in the neuronal constraint base, in order to compensate the modeling errors. The neuronal constraint avoids over-fitting as happened in our previous study (Onizuka et al., 2013) for highly non-stationary experimental data. We further validated that method in (Hoang and Tokuda, 2015), which utilized simulated spike data as the test data. The results on the both data sets confirmed that the Bayesian method has the potential to overcome the problem of non-stationary dynamics and resolved the ill-posedness of the inverse problem. We thus argue that our proposed Bayesian framework is a useful tool to evaluate the parameters of interest in neuroscience.

我们过去研究的主要成就是成功构建了一个复杂的框架，以解决从下橄榄神经元的尖峰序列估计间隙连接和抑制电导的问题（Hoang等人，2015年）的报告。传统上，基于多个神经元的尖峰时间来估计神经元网络的模型参数的问题是严重不适定的。因此，该逆问题需要一种随机方法，该方法在许多可能的解中找到最可能的解。我们开发了一个理论框架的贝叶斯估计，它允许分段波动的参数估计神经元的约束基础，以补偿建模误差。神经元约束避免了如在我们先前的研究中发生的过度拟合（Onizuka等人，2013年），用于高度非平稳的实验数据。我们在（Hoang和Tokuda，2015）中进一步验证了该方法，该方法使用模拟加标数据作为试验数据。两个数据集上的结果证实了贝叶斯方法有潜力克服非平稳动力学的问题，并解决了逆问题的不适定性。因此，我们认为，我们提出的贝叶斯框架是一个有用的工具，以评估在神经科学中感兴趣的参数。

项目成果

Identification of complex spikes in two-photon recording of Purkinje cell calcium responses by machine learning and ROC analysis of the performance

通过机器学习和性能 ROC 分析识别浦肯野细胞钙反应双光子记录中的复杂尖峰

• 发表时间: 2015
• 作者: Huu Hoang;Shinichiro Tsutsumi;Miki Hashizume;Okito Yamashita;Isao T. Tokuda;Masa-aki Sato;Mitsuo Kawato;Masanobu Kano;Kazuo Kitamura & Keisuke Toyama
• 通讯作者: Kazuo Kitamura & Keisuke Toyama

A segmental Bayes to estimate model parameters from neuronal spike trains with complicated dynamics

分段贝叶斯估计具有复杂动力学的神经元尖峰序列的模型参数

• 发表时间: 2015
• 作者: Huu Hoang;Shinichiro Tsutsumi;Miki Hashizume;Okito Yamashita;Isao T. Tokuda;Masa-aki Sato;Mitsuo Kawato;Masanobu Kano;Kazuo Kitamura & Keisuke Toyama;Huu Hoang & Isao T. Tokuda
• 通讯作者: Huu Hoang & Isao T. Tokuda

Verification of parameter estimation techniques from spike train data

根据尖峰序列数据验证参数估计技术

• 发表时间: 2015
• 作者: Huu Hoang;Okito Yamashita;Isao T. Tokuda;Kazuo Kitamura;Masa-aki Sato;Mitsuo Kawato & Keisuke Toyama;Huu Hoang & Isao T. Tokuda
• 通讯作者: Huu Hoang & Isao T. Tokuda

A step-wise Bayes to estimate the gap-junctional and inhibitory conductance of Inferior olive neurons from spike trains

逐步贝叶斯估计来自尖峰序列的下橄榄神经元的间隙连接和抑制电导

• 发表时间: 2014
• 作者: Huu Hoang;Isao T. Tokuda;Okito Yamashita;Masa-aki Sato;Mitsuo Kawato & Keisuke Toyama
• 通讯作者: Mitsuo Kawato & Keisuke Toyama

Hyperacuity Bayesian technique to enhance temporal resolution of two-photon recording of complex spikes of cerebellar Purkinje cells

超敏锐贝叶斯技术提高小脑浦肯野细胞复杂尖峰双光子记录的时间分辨率

• 发表时间: 2016
• 作者: Huu Thien Hoang, Okito Yamashita;Isao T. Tokuda;Kazuo Kitamura; Masa-Aki Sato;Mitsuo Kawato;Keisuke Toyama
• 通讯作者: Keisuke Toyama