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

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).