Abstract:Big data contains the high social,economic,and scientific value.It has been being spotlighted all around the academia and industry.The key technology can be divide into three levels:the data platform,the analysis platform,and the presentation platform.Among these,analysis platform is the bridge that transforms big data into real value.Generally,there are four specific attributes of big data,known as the volume,variety,velocity,and value.They extend the value space of big data.Meanwhile,they become great challenges of big data analysis.There are three major issues:Multi-sourced and heterogeneous big data,storage of tremendous unstructured data,and sparse value in fast changing big data.The three central scientific problems in big data analysis are representation,storage,and prediction of big data.Traditional methods cannot handle big data well.New methods for big data are imperative.Human brains are naturally excellent big data processor.Neural networks are computational replications of big data analysis principles in human brains.Neural networks are the most successful methods for big data analysis.Simulating neural structure in the brain to build neural network structure models and simulating memory mechanism in the brain to develop learning algorithms are two basic methodology in neural networks research.The history of neural networks research has ups and downs.Today,with the support from developing computational power,big data analysis using neural networks has achieved great success,especially in big data applications,for example,audio big data analysis,visual big data analysis,medical big data analysis.Neural networks are leading the artificial-intelligence researches.AlphaGo beating human champion in Go game attracted even more public interest."Big data+Neural Networks" is becoming one of the driving forces of innovation,social promotion,and living development.It can be clearly demonstrated how perfectly matched and mutually reinforcing are big data and neural networks by reviewing the basic concepts and key technology in big data as well as inducing the research framework of neural networks.On the one hand,neural networks are capable for extracting abstract features from raw data.They can combine multiple information sources,process heterogeneous data,and capture dynamic change in data.They are the bridge to implementation of big data value transformation.On the other hand,large volume of big data provide tremendous training samples which enable the training of neural networks with large number of parameters.However,there are still some problems in the "big data+neural networks" pattern.In the aspect of neural networks research,the structure is needing further investigation and development;the scale of network is lacking theoretical guideline;and the learning algorithm is having some inherent problem.In the aspect of big data there are also three core scientific problems:how to ensure the consistency in high-dimensional sparse space;how to implement the "knowledge-only" storage;and how to depict temporal correlation and implement big data prediction.Much more investigations in this area are still in urgent need,including those from theoretical and practical aspects.In particular,cross domain researches are important.Research in the area can be act in coordination with that in understanding big data processing in human brains.More combination with cognitive science and neuroscience are needed to resolve the fundamental scientific problems in neural networks research and big data,in which way the research of big data analysis using neural networks could be improved.