项目名称: 混合学习机制下神经网络的自组织演化与复杂分层结构的形成
项目编号: No.61304165
项目类型: 青年科学基金项目
立项/批准年度: 2014
项目学科: 自动化技术、计算机技术
项目作者: 李秀敏
作者单位: 重庆大学
项目金额: 25万元
中文摘要: 自组织神经网络是认知神经动力学领域的一个重要研究方向,神经网络结构的自组织演化能有效提高网络的功能特性。目前,相关研究主要只针对单一学习机制的自组织神经网络。近期大量实验研究发现,神经元之间的突触连接在不同空间尺度上具有不同的连接形式和学习规律。这一神经生物学现象为研究脑网络中广泛存在的复杂分层结构的形成机理提供了重要依据。本项目拟采用突触的混合学习机制,即对称与非对称放电时间依赖的突触可塑性,对不同空间尺度上的突触连接采用不同的突触学习算法,从而构建具有模块及中心节点分层结构的自组织神经网络。通过分析神经网络的时空动力学特性,探索分层结构对网络自组织临界动力学的影响,从混合学习机制角度刻划脑网络中结构与功能的相互关系。本项目有关研究成果不仅有助于揭示脑网络中复杂结构的形成规律,同时对深入理解大规模神经网络中的高效信号传导机制以及发展和完善自组织神经网络在人工智能计算中的应用具有重要意义。
中文关键词: 自组织神经网络;神经动力学;复杂网络;突触可塑性;自组织临界
英文摘要: Self-organized neural network is an important research topic in the field of cognitive neurodynamics. The self-organization of network structure can significantly enhance the functional properties of neural network. So far, most of related studies only consider one single learning rule for the self-organization of neural networks. However, recently plenty of experimental studies have discovered that synaptic connections among neurons have different connective structures and learning strategies, depending on the spatial scales of connectivity. This neurophysiological phenomenon provides important evidience for exploring the formation mechanisms of the complex hierarchical structure, which prevalently exist in neocortex. Hence, in this project we will present a novel strategy to establish hierarchical neural networks with modules and hub-notes, which can be developed from the self-organization of neural networks by applying hybrid synaptic learning rules, i.e. symmetric and asymmetric spike-timing-dependent plasticity, on the synaptic connections with different spatial scales. Meanwhile, by analyzing the spatio-temporal properties of the neural network, the influence of hierarchical structure on the dynamics of self-organized criticality will be explored. The outcome of this research will be helpful for exploring
英文关键词: self-organized neural network;neural dynamics;complex network;synaptic plasticity;self-organized criticality