A hybrid neural network of addressable and content-addressable memory.
We investigate the memory structure and retrieval of the brain and propose a hybrid neural network of addressable and content-addressable memory which is a special database model and can memorize and retrieve any piece of information (a binary pattern) both addressably and content-addressably. The architecture of this hybrid neural network is hierarchical and takes the form of a tree of slabs which consist of binary neurons with the same array. Simplex memory neural networks are considered as the slabs of basic memory units, being distributed on the terminal vertexes of the tree. It is shown by theoretical analysis that the hybrid neural network is able to be constructed with Hebbian and competitive learning rules, and some other important characteristics of its learning and memory behavior are also consistent with those of the brain. Moreover, we demonstrate the hybrid neural network on a set of ten binary numeral patters
Ma
Jinwen
J
Department of Information Science, School of Mathematical Sciences, and LMAM, Peking University, Beijing, 100871, China. jwma@math.pku.edu.cn
D001185:Artificial Intelligence; D001921:Brain / Q000166:cytology / Q000502:physiology; D003198:Computer Simulation; D001330:Electronic Data Processing; D007858:Learning; D008568:Memory; D008959:Models, Neurological; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology