Epilepsy as a self-organization process: a computational model

Author

Bondarenko, Vladimir E.

Author_Institution

Inst. of Biochem. Phys., Acad. of Sci., Moscow, Russia

fYear

1995

fDate

20-23 Sep 1995

Firstpage

108

Lastpage

114

Abstract

Chaos in the human brain and artificial neural networks is explained with a view to an understanding of human brain functions. Different chaotic solutions are known in neural network modelling, but the comparison of their quantitative characteristics with the human or animal EEGs can not be performed. In this work a model of the start and spread of epilepsy, based on neural nets, is presented. It is shown that the epilepsy-like phenomena can occur in the neural networks with increasing neuronal excitability. The dynamics of the quantitative EEG characteristics (correlation dimension, amplitude, largest Lyapunov exponent) is similar to one at the onset of epilepsy

Keywords

brain models; chaos; neurophysiology; self-organising feature maps; amplitude; artificial neural networks; chaos; computational model; correlation dimension; epilepsy; human brain; largest Lyapunov exponent; neuronal excitability; quantitative EEG characteristics; self-organization process; Artificial neural networks; Biological neural networks; Brain modeling; Chaos; Computational modeling; Delay effects; Electroencephalography; Epilepsy; Humans; Neurons;

fLanguage

English

Publisher

ieee

Conference_Titel

Neuroinformatics and Neurocomputers, 1995., Second International Symposium on

Conference_Location

Rostov on Don

Print_ISBN

0-7803-2512-5

Type

conf

DOI

10.1109/ISNINC.1995.480843

Filename

480843