title: Adaptive learning algorithms for nernst potential and I-V curves in nerve cell membrane ion channels modeled as hidden Markov models.
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.
authors.affiliation: Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC, Canada. vikramk@ece.ubc.ca
authors.forename: Vikram
authors.initials: V
authors.lastname: Krishnamurthy
authors.forename: Shin-Ho
authors.initials: SH
authors.lastname: Chung
mesh_terms: D000465:Algorithms; D000818:Animals; D001185:Artificial Intelligence; D002462:Cell Membrane / Q000502:physiology*; D003198:Computer Simulation; D017097:Electric Impedance; D005246:Feedback; D006801:Humans; D015640:Ion Channel Gating / Q000502:physiology; D007473:Ion Channels / Q000502:physiology*; D008390:Markov Chains; D008564:Membrane Potentials / Q000502:physiology*; D008959:Models, Neurological; D015233:Models, Statistical; D009474:Neurons / Q000502:physiology*; D018408:Patch-Clamp Techniques / Q000379:methods
chemical_list: D007473:Ion Channels