Knowledge extraction from evolving spiking neural networks with rank order population coding.
This paper demonstrates how knowledge can be extracted from evolving spiking neural networks with rank order population coding. Knowledge discovery is a very important feature of intelligent systems. Yet, a disproportionally small amount of research is centered on the issue of knowledge extraction from spiking neural networks which are considered to be the third generation of artificial neural networks. The lack of knowledge representation compatibility is becoming a major detriment to end users of these networks. We show that a high-level knowledge can be obtained from evolving spiking neural networks. More specifically, we propose a method for fuzzy rule extraction from an evolving spiking network with rank order population coding. The proposed method was used for knowledge discovery on two benchmark taste recognition problems where the knowledge learnt by an evolving spiking neural network was extracted in the form of zero-order Takagi-Sugeno fuzzy IF-THEN rules.
School of Electrical Engineering, Manukau Institute of Technology, Auckland, New Zealand. ssoltic@manukau.ac.nz
Snjezana
S
Soltic
Nikola
N
Kasabov
D000200:Action Potentials / Q000502:physiology*; D000465:Algorithms; D000818:Animals; D017143:Fuzzy Logic; D006801:Humans; D016247:Information Storage and Retrieval; D019359:Knowledge; D008959:Models, Neurological; D009415:Nerve Net / Q000166:cytology* / Q000502:physiology; D009474:Neurons / Q000502:physiology*