Abstract neural automata: variability of structure, thought and Riemannian volume

被引:0
作者
Xi, GC [1 ]
机构
[1] Chinese Acad Sci, Inst Automat, Beijing, Peoples R China
关键词
cybernetics; neural networks;
D O I
10.1108/03684920210428416
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
Believes that in the view of philosophy, a concept is the highest form of activity of human brain. This paper demonstrates Abstract Neural Automata and a more perfect brain's models that have the ability of transition of concept-ability of thought. The transition of the concept of Abstract Neural automata results from the non-uniqueness of its limit Gibbs measure-variability of the structure of Abstract Neural Automata. By means of topological conjugate transformation, the previous theory of Abstract Neural Automata on a d-dimensional (d greater than or equal to 1) integer lattice is extended to the compact Riemannian manifold. We have pointed out emphatically that functions of cognition and thought of Abstract Neural Automata depend crucially on its topological and the Riemannian structure, particularly, on its Riemannian volume of some relative places which are relative learning, memory, cognition and thought. Furthermore, the larger the Riemannian volume, the stronger the intelligent function. In the study of the human brain, and in particular, Einstein's brain, one has discovered such information.
引用
收藏
页码:130 / 139
页数:10
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