Identification of functional connectivity of biological neural network based on mutual information

LIU Jian-zhao; DONG Chao-yi; FENG Li-fei

doi:10.13873/J.1000-9787(2016)06-0059-03

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Identification of functional connectivity of biological neural network based on mutual information

更新时间：2025-09-11

- Identification of functional connectivity of biological neural network based on mutual information
- Vol. 35, Issue 6, Pages: 59-61+65(2016)
- 作者机构：
  
  1. 内蒙古工业大学电力学院
  2. 内蒙古机电控制重点实验室
- 作者简介：
- 基金信息：
- DOI：10.13873/J.1000-9787(2016)06-0059-03
  CLC： TP183
- Published：2016
- 稿件说明：
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[1]刘剑钊,董朝轶,冯丽斐.基于互信息的生物神经网络功能性连接辨识[J].传感器与微系统,2016,35(06):59-61+65.

LIU Jian-zhao, DONG Chao-yi, FENG Li-fei. Identification of functional connectivity of biological neural network based on mutual information[J]. 2016, 35(6): 59-61+65.
[1]刘剑钊,董朝轶,冯丽斐.基于互信息的生物神经网络功能性连接辨识[J].传感器与微系统,2016,35(06):59-61+65. DOI： 10.13873/J.1000-9787(2016)06-0059-03.

LIU Jian-zhao, DONG Chao-yi, FENG Li-fei. Identification of functional connectivity of biological neural network based on mutual information[J]. 2016, 35(6): 59-61+65. DOI： 10.13873/J.1000-9787(2016)06-0059-03.

摘要

生物神经网络（BNN）功能性连接的辨识方法被广泛地应用于使用BNN的多通道时间序列数据构建网络连接结构

帮助加深对BNN结构和功能间关系的认识和理解。首先

建立基于积分点火（IF）机制的BNN模型

获得多通道神经元脉冲序列;然后

运用互信息（MI）方法计算出各神经元间的MI值

超过一定阈值的MI表明两个神经元间存在相互连接关系。仿真结果表明:基于MI的网络辨识方法计算开销较小

对BNN功能性连接结构具有较高的辨识度。

Abstract

The identification methods of biological neural network（ BNN） functional connections have been widely used in building network connecting structure with multi-channel time series data of BNN. The investigation of connective structures of BNN helps to further deepen awareness and understanding of their relations to various network functions of BNN. First

synthetic BNN models are established using integrate-and-fire（ IF） mechanism

and multi-channel pulse series data are generated form the network. Then

using mutual information（ MI） method

MI between the two neuron nodes can be calculated

which exceeds a certain threshold value indicating that there is a connection between the two neurons. Simulation result shows that the network identification method based on MI has a small computational cost

also it has a high accuracy for identifying the BNN functional connection structures.

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references

基于贝叶斯网络模型的遥感图像数据处理技术 [J]. 李启青,马建文,哈斯巴干,韩秀珍,刘志丽. 电子与信息学报 . 2003(10)

神经生物学[M]. 高等教育出版社 , 寿天德主编, 2006

Inferring gene regulatory networks from temporal expression profiles under time-delay and noise [J] . Shinuk Kim,Junil Kim,Kwang-Hyun Cho. Computational Biology and Chemistry . 2007 (4)

Effect of an exponentially decaying threshold on the firing statistics of a stochastic integrate-and-fire neuron [J] . Benjamin Lindner,André Longtin. Journal of Theoretical Biology . 2004 (4)

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