DocumentCode :
3267188
Title :
Parameter and input estimation in Hindmarsh-Rose neuron by adaptive observer
Author :
Mukae, Jun ; Totoki, Yusuke ; Suemitsu, Haruo ; Matsuo, Takami
Author_Institution :
Grad. Sch. of Eng., Oita Univ., Oita, Japan
fYear :
2011
fDate :
20-22 Dec. 2011
Firstpage :
1090
Lastpage :
1095
Abstract :
In this paper, we present adaptive observers of Hindmarsh-Rose (HR) neurons with the membrane potential measurement under the assumption that all parameters in an individual HR neuron are unknown. Using the time-varying adaptive observer for MIMO systems, we propose two adaptive observers to identify the firing pattern of a model of synaptically coupled HR neurons. The procedure allows us to recover the internal states and to distinguish the firing patterns of the synaptically coupled HR neurons, with early-time dynamic behaviors.
Keywords :
MIMO systems; neural nets; observers; parameter estimation; pattern recognition; time-varying systems; Hindmarsh-Rose neuron; MIMO system; firing pattern identification; input estimation; internal state recovery; membrane potential measurement; parameter estimation; synaptically coupled HR neurons; time-varying adaptive observer; Adaptation models; Adaptive systems; Computational modeling; Mathematical model; Neurons; Observers;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
System Integration (SII), 2011 IEEE/SICE International Symposium on
Conference_Location :
Kyoto
Print_ISBN :
978-1-4577-1523-5
Type :
conf
DOI :
10.1109/SII.2011.6147601
Filename :
6147601
Link To Document :
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