Firing Pattern Estimation of Synaptically Coupled Hindmarsh-Rose Neurons by Adaptive Observer

Totoki, Yusuke; Mitsunaga, Kouichi; Suemitsu, Haruo; Matsuo, Takami

doi:10.1007/978-3-540-87559-8_35

Cited by 2 publications

(1 citation statement)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Deterministic state estimation methods at the microscopic level (single neuron) include works by Tokuda et al in [27], Totoki et al in [28], Tyukin et al in [29] and Mao et al in [30]. As our measurement of choice is the EEG, which is known to reflect the average behaviour of populations of neurons [31], our work differs by taking a macroscopic view.…”

Section: Introductionmentioning

confidence: 99%

Estimating the unmeasured membrane potential of neuronal populations from the EEG using a class of deterministic nonlinear filters

et al. 2012

View full text Add to dashboard Cite

We present a model-based estimation method to reconstruct the unmeasured membrane potential of neuronal populations from a single-channel electroencephalographic (EEG) measurement. We consider a class of neural mass models that share a general structure, specifically the models by Stam et al (1999 Clin. Neurophysiol. 110 1801-13), Jansen and Rit (1995 Biol. Cybern. 73 357-66) and Wendling et al (2005 J. Clin. Neurophysiol. 22 343). Under idealized assumptions, we prove the global exponential convergence of our filter. Then, under more realistic assumptions, we investigate the robustness of our filter against model uncertainties and disturbances. Analytic proofs are provided for all results and our analyses are further illustrated via simulations.

show abstract