摘要
The problem of representation is the most kernel and most key problem in artificial intelligence. The hy-pothesis of inconsequence of the lower order structure is our traditional choice, and use standing instead of standingfor to implement representation. Stemming from computational cognitive neuroscience, thus based on neuron infor-mation processing, massive representation by neural network and cognitive behaviors' dynamics model of neural sys-tem, this research aims to build a common and consistent groundwork within neuroscience to explain kinds of intelli-gent behaviors, and fuse indormation, system and processing procedure together fluently. This research is great sig-nificant for the structure simulation method of AI and the probe of intelligence's neural mechanism.
The problem of representation is the most kernel and most key problem in artificial intelligence. The hypothesis of inconsequence of the lower order structure is our traditional choice, and use standing instead of standing for to implement representation. Stemming from computational cognitive neuroscience, thus based on neuron information processing, massive representation by neural network and cognitive behaviors' dynamics model of neural system, this research aims to build a common and consistent groundwork within neuroscience to explain kinds of intelligent behaviors, and fuse information, system and processing procedure.together fluently. This research is great significant for the structure simulation method of AI and the probe of intelligence's neural mechanism.
出处
《计算机科学》
CSCD
北大核心
2003年第7期144-146,共3页
Computer Science
基金
复旦大学青年科学基金(项目批准号01-13)
