Makarov Valeri A, Panetsos Fivos, de Feo Oscar
Neuroscience Laboratory, Department of Applied Mathematics, School of Optics, Universidad Complutense de Madrid, Avda. Arcos de Jalon s/n, 28037 Madrid, Spain.
J Neurosci Methods. 2005 Jun 15;144(2):265-79. doi: 10.1016/j.jneumeth.2004.11.013. Epub 2004 Dec 21.
In the present paper we propose a novel method for the identification and modeling of neural networks using extracellular spike recordings. We create a deterministic model of the effective network, whose dynamic behavior fits experimental data. The network obtained by our method includes explicit mathematical models of each of the spiking neurons and a description of the effective connectivity between them. Such a model allows us to study the properties of the neuron ensemble independently from the original data. It also permits to infer properties of the ensemble that cannot be directly obtained from the observed spike trains. The performance of the method is tested with spike trains artificially generated by a number of different neural networks.
在本文中，我们提出了一种使用细胞外尖峰记录来识别和建模神经网络的新方法。我们创建了有效网络的确定性模型，其动态行为与实验数据相符。通过我们的方法获得的网络包括每个发放脉冲神经元的显式数学模型以及它们之间有效连接性的描述。这样的模型使我们能够独立于原始数据来研究神经元集合的特性。它还允许推断从观察到的尖峰序列中无法直接获得的集合特性。该方法的性能通过由多个不同神经网络人工生成的尖峰序列进行测试。
