Adaptive learning algorithms for nernst potential and I-V curves in nerve cell membrane ion channels modeled as hidden Markov models.
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.
Krishnamurthy
Vikram
V
Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC, Canada. vikramk@ece.ubc.ca
Chung
Shin-Ho
SH
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
D007473:Ion Channels