脉冲神经网络的新结构与学习算法研究

项目名称： 脉冲神经网络的新结构与学习算法研究

项目编号： No.11201051

项目类型： 青年科学基金项目

立项/批准年度： 2013

项目学科： 数理科学和化学

项目作者： 杨洁

作者单位： 大连理工大学

项目金额： 22万元

中文摘要： 脉冲神经网络由于对生物神经系统的高度仿真性已经受到越来越多的关注，被称为第三代神经网络。本项目旨在研究新的脉冲神经网络结构和更好的学习算法。首先，针对我们最近提出的一种新的网络结构,研究如何将脉冲激发强度(即状态函数x(t)达到阈值时的导数)嵌入到脉冲网络的网络构造与运行机制中去，探索脉冲激发强度对突触后神经元信息处理能力的影响程度与影响方式，同时进一步探寻其它影响脉冲激发因素，使脉冲神经网络在不影响训练精度前提下减小网络规模,改善推广精度。其次，结合申请者主持的前一个国家自然科学基金的工作,研究脉冲神经网络的模糊化问题，提出几种基于s-t模的模糊脉冲神经网络结构,从而扩展脉冲神经网络的信息处理能力。另外，我们已经证明传统BP算法也完全适用于脉冲神经网络，希望以此为基础,对脉冲神经网络的BP算法及其它学习算法进行分析与比较,探讨适用于脉冲神经网络的更高效的学习算法。

中文关键词： 脉冲神经网络；BP算法；鲁棒性；L_1/2 正则化；

英文摘要： With the high simulation to biological neural systems, the spiking neural network has increasingly attracted attentions and is called the third generation neural network. However, it has a large computation and its accuracy is easily affected by the network's structure. These disadvantages are considered the brief bottlenecks of this network. The aim of this project is to improve the network and to accelerate learning through the studies of network constitution methods and learning algorithms. Firstly, the problem of embedding the spike stimulating strength (the gradient of the state function at the firing time) is studied. With this approach, we will explore the influence degree and pattern of the spike stimulating strength on the information processing ability of the postsynaptic neuron. Meanwhile, we will further seek other factors which effect the neuron stimulating to present improved network operation mechanism. Secondly, with the research results funded by the prior National Natural Science Foundation, we will study the fuzzification of spiking neural networks and present several network structures which are based on s-t norms to expand the information processing ability for spiking neural networks. In addition, as for learning algorithms, we have proved that the traditional BP algorithm is fully suitabl

英文关键词： Spiking neural network；BP algorithm；Robustness；L_1/2 regularization；