Adaptive learning algorithms for nernst potential and I-V curves in nerve cell membrane ion channels modeled as hidden Markov models

IEEE Transactions on Nanobioscience
Vikram Krishnamurthy, Shin-Ho Chung

Abstract

We present discrete stochastic optimization algorithms that adaptively learn the Nernst potential in membrane ion channels. The proposed algorithms dynamically control both the ion channel experiment and the resulting hidden Markov model signal processor and can adapt to time-varying behavior of ion channels. One of the most important properties of the proposed algorithms is their self-learning capability--they spend most of the computational effort at the global optimizer (Nernst potential). Numerical examples illustrate the performance of the algorithms on computer-generated synthetic data.

References

Sep 29, 1990·Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences·S H ChungP W Gage
Dec 30, 1991·Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences·S H ChungJ B Moore
Aug 1, 1981·Pflügers Archiv : European journal of physiology·O P HamillF J Sigworth
Mar 22, 2003·Proceedings of the National Academy of Sciences of the United States of America·Megan O'MaraShin-Ho Chung

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Citations

Oct 12, 2004·IEEE Transactions on Nanobioscience·Vikram Krishnamurthy

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