A hybrid neural network model for consciousness.
A new framework for consciousness is introduced based upon traditional artificial neural network models. This framework reflects explicit connections between two parts of the brain: one global working memory and distributed modular cerebral networks relating to specific brain functions. Accordingly this framework is composed of three layers, physical mnemonic layer and abstract thinking layer, which cooperate together through a recognition layer to accomplish information storage and cognition using algorithms of how these interactions contribute to consciousness: (1) the reception process whereby cerebral subsystems group distributed signals into coherent object patterns; (2) the partial recognition process whereby patterns from particular subsystems are compared or stored as knowledge; and (3) the resonant learning process whereby global workspace stably adjusts its structure to adapt to patterns' changes. Using this framework, various sorts of human actions can be explained, leading to a general approach for analyzing brain functions.
Lin
Jie
J
Institute of Artificial Intelligence, Zhejiang University, Hangzhou 310027, China. leadtek@zju.edu.cn.
Jin
Xiao-gang
XG
Yang
Jian-gang
JG
D000465:Algorithms; D001921:Brain / Q000502:physiology*; D003198:Computer Simulation; D003243:Consciousness / Q000502:physiology*; D008568:Memory / Q000502:physiology*; D008959:Models, Neurological; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*