Open AccessThe role of inhibitory neuron in a delayed neural network
Author(s) -
Zhanshan Wang,
Huaguang Zhang
Publication year - 2006
Publication title -
wuli xuebao
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.199
H-Index - 47
ISSN - 1000-3290
DOI - 10.7498/aps.55.5674
Subject(s) - inhibitory postsynaptic potential , artificial neural network , connection (principal bundle) , equilibrium point , computer science , embedding , order (exchange) , stability (learning theory) , control theory (sociology) , transmission (telecommunications) , point (geometry) , topology (electrical circuits) , mathematics , neuroscience , biology , mathematical analysis , artificial intelligence , combinatorics , telecommunications , geometry , control (management) , finance , machine learning , economics , differential equation
The role of inhibitory self-connection in a second order recurrent neural network with delays has been investigated. A sufficient condition is proposed to guarantee the global asymptotical stability of the equilibrium point for the delayed neural network. The results indicate that an unstable neural network without inhibitory interconnections can be asymptotically stabilized to a unique equilibrium point via embedding inhibitory self-connections with proper strengths, and the role of inhibitory self-connections will be restricted by the magnitude of transmission delays. Two simulation examples are used to show the effectiveness of the obtained result.