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

TitleAdaptive learning algorithms for Nernst potential and I-V curves in nerve cell membrane ion channels modeled as hidden Markov models
Publication TypeJournal Article
Year of Publication2003
AuthorsKrishnamurthy, V., and S. - H. Chung
JournalNanoBioscience, IEEE Transactions on
Volume2
Pagination266 -278
Date Publisheddec.
ISSN1536-1241
Keywordsadaptive learning algorithms, Algorithms, Animals, Artificial Intelligence, bioelectric phenomena, biology computing, biomembrane transport, cell membrane, computer simulation, electric impedance, feedback, global optimizer, hidden Markov model signal processor, hidden Markov models, Humans, I-V curves, ion channel experiment, Ion Channel Gating, ion channels, learning (artificial intelligence), Markov Chains, Membrane Potentials, Models, Nernst potential, nerve cell membrane ion channels, Neurological, neurons, neurophysiology, Patch-Clamp Techniques, Statistical, stochastic optimization algorithms, stochastic processes, time-varying behavior
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 its 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.

URLhttp://dx.doi.org/10.1109/TNB.2003.820275
DOI10.1109/TNB.2003.820275

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