复杂网络体系下脑联想记忆功能网络的稀疏拓扑择优与建模研究

项目名称： 复杂网络体系下脑联想记忆功能网络的稀疏拓扑择优与建模研究

项目编号： No.61304122

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： 杨静

作者单位： 中国科学院合肥物质科学研究院

项目金额： 22万元

中文摘要： 模拟生物脑神经认知系统的联想记忆机制，是仿生智能信息处理研究中的关键科学问题之一。建立在全互联结构上的联想记忆模型，只关注固定结构下学习算法的实现，缺乏对于生物脑神经系统建模的合理性，且不具备网络结构自组织学习的柔性，智能水平有限。本课题以探索新型联想记忆模型的实现方式为研究目标，借鉴生物脑神经系统中普适存在的复杂网络特性，以复杂网络结构与功能间关系的研究为切入点，使用非平衡态统计分析方法研究联想记忆在复杂网络拓扑结构上涌现的动力学行为，从理论分析、数值仿真及两者的一致性验证等方面研究复杂网络稀疏互联方式对其联想记忆性能的影响；并在此基础上，研究基于启发式退火拓扑择优机制的网络稀疏结构自组织学习方法，构建具有脑神经生物学依据的复杂网络小世界及无标度联想记忆模型。本课题的研究成果将为解释脑中智能复杂性与拓扑复杂性之间的联系提供理论与模型支持，在面向模式识别的仿生智能信息处理领域具有应用价值。

中文关键词： 联想记忆；复杂网络；稀疏互联；启发式退火拓扑择优；

英文摘要： Associative memory modeling human intelligence as information storage and recovery is a hot research issue in cognitive neural computing, and receives widely application in the field of artificial intelligence and pattern recognition. But current models built on fully connected network focus on algorithm realization under fixed structure, therefore they are lack of biological modeling background and self-organized learning flexibility of the network topology and thus the intelligent level of these models is limited. Complex network focuses on the relationship between topology and function of the network, and it's a brand-new method of complex system investigation. Associative memory model is a dynamical nonlinear complex system in essence, small-word and scale-free characteristics are universal phenomenon in biological neural system. Therefore, from these points of view, it's a novel and feasible manner to study associative memory through complex network ideology. In this project, we utilize complex network ideology to research the influence of the neurons' sparse interconnection style on the network performance, i.e. topology versus function. Starting from the network topology, we do detailed theoretical analysis through non-equilibrium statistical method and also complementary numerical investigation. In the v

英文关键词： Associative Memory；Complex Network；Sparsely Connected；Heuristic Annealed Topological Preferential；