Connection‐Strength Estimation of Neuronal Networks by Fitting for Izhikevich Model
Connection‐Strength Estimation of Neuronal Networks by Fitting for Izhikevich Model
复制标题
通过拟合 Izhikevich 模型来估计神经网络的连接强度
DOI：
10.1002/eej.22517
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发表时间：
2014
影响因子：
0.4
通讯作者：
Y. Jimbo
中科院分区：
文献类型：
--
作者：
Takuya Isomura;Akimasa Takeuchi;K. Shimba;K. Kotani;Y. Jimbo
近年来，利用多神经元记录进行了大量的研究，但所得到的峰值时间序列体积大、结构复杂，在特征提取方面存在很多问题。本文介绍了一种利用极大似然估计方法拟合Izhikevich模型来估计神经元间突触连接强度的新方法。我们证明了我们的方法可以从模拟神经集合给出的峰值时间序列中估计连接强度，并且可以估计两个独立的培养神经网络之间的非连通性。这些结果表明我们的方法适用于神经网络的网络和可塑性分析。
Recently, there has been abundant research using multineuron recording, but there are many problems with extracting the features from the obtained spike time series, which are huge in volume and complex. Here we introduce a new method of estimating synaptic connection strengths between neurons by fitting to the Izhikevich model by maximum likelihood estimation. We demonstrate that our method can estimate connection strengths from spike time series given by a simulated neural ensemble and can estimate nonconnectivity between two independent cultured neuronal networks. These results suggest that our method is applicable to network and plasticity analysis of neuronal networks.