Learning network structures from firing patterns
Learning network structures from firing patterns
复制标题
从发射模式学习网络结构
DOI：
10.1109/icassp.2016.7471765
复制
发表时间：
2016
期刊：
影响因子：
--
通讯作者：
M. Vetterli
中科院分区：
文献类型：
--
作者：
Amin Karbasi;A. Salavati;M. Vetterli
我们如何基于有限的观察结果破译网络的隐藏结构？这个问题在许多情况下从社交到无线到神经网络不等。在这种情况下，我们通常会观察到节点的行为（例如，节点学习了一段信息或节点被疾病感染的时间），并且我们有兴趣推断出扩散的真实网络。发生。在本文中，我们在神经网络上考虑了这个问题，我们的目的是仅通过观察其发射活动来重建神经元之间的连通性。我们开发了一种迭代性神经推断算法（NEUINF），以确定基于感知的学习规则的有效神经联系（即兴奋性/抑制性）的类型。我们为NEUINF的平均性能以及数值分析提供了理论界限，以比较提出的先前ART方法的性能。
How can we decipher the hidden structure of a network based on limited observations? This question arises in many scenarios ranging from social to wireless and to neural networks. In such settings, we typically observe the nodes' behaviors (e.g., the time a node learns about a piece of information, or the time a node gets infected by a disease), and we are interested in inferring the true network over which the diffusion takes place. In this paper, we consider this problem over a neural network where our aim is to reconstruct the connectivity between neurons merely by observing their firing activity. We develop an iterative NEUral INFerence algorithm (NeuInf) to identify the type of effective neural connections (i.e. excitatory/inhibitory) based on the Perceptron learning rule. We provide theoretical bounds on the average performance of NEUINF as well as numerical analysis to compare the performance of the proposed approach to previous art.
影响因子：
4.3
作者：
Gerhard F;Kispersky T;Gutierrez GJ;Marder E;Kramer M;Eden U
通讯作者：
Eden U