Skip navigation
Skip navigation

Adaptive Learning Algorithms for Nernst Potential and I-V Curves in Nerve Cell Membrane Ion Channels Modelled as Hidden Markov Models

Krishnamurthy, Vikram; Chung, Shin-Ho

Description

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

dc.contributor.authorKrishnamurthy, Vikram
dc.contributor.authorChung, Shin-Ho
dc.date.accessioned2015-12-13T23:12:13Z
dc.date.available2015-12-13T23:12:13Z
dc.identifier.issn1536-1241
dc.identifier.urihttp://hdl.handle.net/1885/87947
dc.description.abstractWe 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
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE Inc)
dc.sourceIEEE Transactions on Nanobioscience
dc.subjectKeywords: Approximation theory; Cell membranes; Electric potential; Mathematical models; Statistical methods; Stochastic control systems; Discrete stochastic approximation; Hidden markov models (HMM); Ion channel currents; Nernst potential; Learning algorithms Discrete stochastic approximation; Hidden Markov models (HMMs); Ion channel currents; Nernst potential
dc.titleAdaptive Learning Algorithms for Nernst Potential and I-V Curves in Nerve Cell Membrane Ion Channels Modelled as Hidden Markov Models
dc.typeJournal article
local.description.notesImported from ARIES
local.description.refereedYes
local.identifier.citationvolume2
dc.date.issued2003
local.identifier.absfor029901 - Biological Physics
local.identifier.ariespublicationMigratedxPub17442
local.type.statusPublished Version
local.contributor.affiliationKrishnamurthy, Vikram, University of British Columbia
local.contributor.affiliationChung, Shin-Ho, College of Physical and Mathematical Sciences, ANU
local.bibliographicCitation.issue5
local.bibliographicCitation.startpage266
local.bibliographicCitation.lastpage278
local.identifier.doi10.1109/TNB.2003.820275
dc.date.updated2015-12-12T08:30:47Z
local.identifier.scopusID2-s2.0-3042764652
CollectionsANU Research Publications

Download

There are no files associated with this item.


Items in Open Research are protected by copyright, with all rights reserved, unless otherwise indicated.

Updated:  17 November 2022/ Responsible Officer:  University Librarian/ Page Contact:  Library Systems & Web Coordinator