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

IEEE Transactions on Nanobioscience

Volumn 2, Issue 4, 2003, Pages 266-278

  Krishnamurthy, Vikram ^a,b   Chung, Shin Ho ^c  

^a UNIVERSITY OF BRITISH COLUMBIA   (Canada)

^b UNIVERSITY OF MELBOURNE   (Australia)

^c AUSTRALIAN NATIONAL UNIVERSITY   (Australia)

Author keywords

Discrete stochastic approximation; Hidden Markov models (HMMs); Ion channel currents; Nernst potential

Indexed keywords

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;

ION CHANNEL;

ALGORITHM; ANIMAL; ARTICLE; ARTIFICIAL INTELLIGENCE; BIOLOGICAL MODEL; CELL MEMBRANE; CELL MEMBRANE POTENTIAL; CHANNEL GATING; COMPARATIVE STUDY; COMPUTER SIMULATION; EVALUATION; FEEDBACK SYSTEM; HUMAN; IMPEDANCE; METHODOLOGY; NERVE CELL; PATCH CLAMP; PHYSIOLOGY; PROBABILITY; STATISTICAL MODEL; VALIDATION STUDY;

ALGORITHMS; ANIMALS; ARTIFICIAL INTELLIGENCE; CELL MEMBRANE; COMPUTER SIMULATION; ELECTRIC IMPEDANCE; FEEDBACK; HUMANS; ION CHANNEL GATING; ION CHANNELS; MARKOV CHAINS; MEMBRANE POTENTIALS; MODELS, NEUROLOGICAL; MODELS, STATISTICAL; NEURONS; PATCH-CLAMP TECHNIQUES;

EID: 3042764652     PISSN: 15361241     EISSN: None     Source Type: Journal    
DOI: 10.1109/TNB.2003.820275     Document Type: Article

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