Stability of dynamic brain models in neuropercolation approximation

Author

Sokolov, Yury ; Kozma, Robert

Author_Institution

Dept. of Math. Sci., Univ. of Memphis, Memphis, TN, USA

fYear

2014

fDate

5-8 Oct. 2014

Firstpage

2230

Lastpage

2233

Abstract

In this paper, the basic building blocks of the intentional neurodynamics cycle are studied using probabilistic cellular automata. We apply the mean field neuropercolation model to describe the dynamics of coupled excitatory-inhibitory neural populations. The model exhibits a phase transition between a background point attractor and narrow-band (limit-cycle) behavior, depending of the choice of system parameters. Metastable dynamics near criticality is investigated. We show the existence of various regions with unstable and multiple stable equilibria. The results are relevant to modeling cognitive phase transitions observed in brains during awareness experience.

Keywords

cellular automata; cognition; neural nets; probabilistic automata; background point attractor; cognitive phase transitions; coupled excitatory-inhibitory neural populations; dynamic brain model stability; intentional neurodynamics cycle; mean field neuropercolation model; multiple stable equilibria; narrow-band behavior; neuropercolation approximation; phase transition; probabilistic cellular automata; unstable equilibria; Automata; Biological system modeling; Brain modeling; Limit-cycles; Sociology; Stability analysis; Statistics;

fLanguage

English

Publisher

ieee

Conference_Titel

Systems, Man and Cybernetics (SMC), 2014 IEEE International Conference on

Conference_Location

San Diego, CA

Type

conf

DOI

10.1109/SMC.2014.6974256

Filename

6974256