Adaptive discrete stochastic optimization algorithm for learning Nernst potential in nerve cell membrane ion channels

TitleAdaptive discrete stochastic optimization algorithm for learning Nernst potential in nerve cell membrane ion channels
Publication TypeConference Paper
Year of Publication2004
AuthorsKrishnamurthy, V., and S. - H. Chung
Conference NameAcoustics, Speech, and Signal Processing, 2004. Proceedings. (ICASSP '04). IEEE International Conference on
PaginationV - 593-6 vol.5
Date Publishedmay.
Keywordsadaptive discrete stochastic optimization algorithm, adaptive Nernst potential learning, bioelectric potentials, biomembrane transport, global optimizer, hidden Markov model, hidden Markov models, HMM signal processing methods, ion channel experiment dynamic control, ion channel time-varying behaviour, medical signal processing, nerve cell membrane ion channels, optimisation, self-learning algorithms, unsupervised learning
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 (HMM) signal processor and can adapt to the time-varying behaviour of ion channels. One of the most important properties of the proposed algorithms are their self-learning capability - they spends most of the computational effort at the global optimizer (Nernst potential).

URLhttp://dx.doi.org/10.1109/ICASSP.2004.1327180
DOI10.1109/ICASSP.2004.1327180

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