Innovative types and abilities of neural networks based on associative mechanisms and a new associative model of neurons


Autorzy/Authors: Adrian Horzyk

Wydawnictwo/Publisher: Springer Verlag, LNAI 9119, 2015, pp. 26-38.


This paper presents a new concept of representation of data and their relations in neural networks which allows to automatically associate, reproduce them, and generalize about them. It demonstrates an innovative way of developing emergent neural representation of knowledge using a new kind of neural networks whose structure is automatically constructed and parameters are automatically computed on the basis of plastic mechanisms implemented in a new associative model of neurons - called as-neurons. Inspired by the plastic mechanisms commonly occurring in a human brain, this model allows to quickly create associations and establish weighted connections between neural representations of data, their classes, and sequences. As-neurons are able to automatically interconnect representing similar or sequential data. This contribution describes generalized formulas for quick analytical computation of the structure and parameters of ANAKG neural graphs for representing and recalling of training sequences of objects.


associative mechanisms; as-neurons; ANAKG neural graphs; knowledge engineering; knowledge representation; artificial neural associative systems; associative neural networks; emergent cognitive systems; sequence pattern mining.