基于生物脑网络连接特性的高效神经网络算法  

Efficient Neural Networks Algorithm Based on Connectivity Properties of Biological Brain Networks

作　　者：庞艺伟 于玉国 PANG Yi-Wei;YU Yu-Guo(Software School,Fudan University,Shanghai 200433,China;Research Institute of Intelligent Complex Systems,Fudan University,Shanghai 200433,China)

机构地区：[1]复旦大学软件学院,上海200433 [2]复旦大学智能复杂体系基础理论与关键技术实验室,上海200433

出　　处：《计算机系统应用》2023年第5期196-203,共8页Computer Systems & Applications

基　　金：科技部项目(2021ZD0201301)。

摘　　要：人工神经网络发展至今,已经在计算机视觉、类脑智能等方面得到广泛应用.在过去几十年中,人们对神经网络的研究注重追求更高的准确率,从而忽略了对网络计算成本的控制.而人脑作为高效且节能的网络,其对人工智能的发展起到了重要启示作用.如何仿真生物脑网络的连接特性,建立超低能耗的人工神经网络模型实现基本相同的目标识别正确率成为当前研究的热点.为建立低能耗的人工神经网络模型,本文结合大脑网络的连接特性,通过改变人工神经网络的连接实现网络的高效性.实验结果表明,结合生物脑网络的连接特性,改变网络的连接,很大程度上减少了网络的计算成本,而网络的性能并没有受到明显影响.Artificial neural networks have been developed and widely applied in computer vision and brain-like intelligence.In the past decades,research on neural networks focuses on higher accuracy rates but neglects the control of network computational costs.The human brain,as an efficient and energy-saving network,plays an important role in the development of artificial intelligence.How to emulate the connectivity properties of biological brain networks and build an ultra-low energy artificial neural network model for achieving essentially the same correct target recognition rate has become a hot research topic.To build an ultra-low artificial neural network model,this study realizes network efficiency by combining the connection properties of brain networks to change the connections of artificial neural networks.The experimental results show that combining the connectivity properties of biological brain networks to change the connections of the networks largely reduces the computational cost of the network,while the performance of the network is not significantly affected.

关 键 词：高效网络 复杂网络 连接异质性 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]