Öz Hodgkin-Huxley (HH) neuronal model has been widely accepted neuronal model in neuroscience. The variation of the ionic currents in neuron cell causes the variations in the membrane potential. The level of membrane potential indicates the activation and inactivation dynamics. In this paper, in order to observe the unmeasurable states and parameters of HH neuron accurately, Runge-Kutta discretization based nonlinear observer is designed. In numerical simulations, the membrane potential is measured and the ionic currents are estimated. The numerical results provide accurate estimation results that can be used both in monitoring and control of neuron dynamics.
 Dayan, P.; Abbott, L. (2005). Theoretical Neuroscience: Computational &Mathematical modelling of neural systems. ISBN-10: 0262541858. MIT Press.
 Hodgkin, A.; Huxley, A. (1952). A quantitative description of membrane current and its application to conduction and excitation in nerve. The Journal of Physiology 117: (4), 500–544.
 Neefs, P. J.; Steur, E.; Nijmeijer, (2010). H. Network complexity and synchronous behavior - an experimental approach. International Journal of Neural Systems 20: (03), 233–247.
 Dahasert, N.; Öztürk, İ.; Kılıç, R. (2012). Experimental realizations of the HR neuron model with programmable hardware and synchronization applications. Nonlinear Dynamics 70: (4), 2343–2358.
 Li, W.; Cheung, R.; Chan, R.; Song, D.; Berger, T. (2013). Real-time prediction of neuronal population spiking activity using fpga. Biomedical Circuits and Systems, IEEE Transactions on 7: (4), 489–498.
 Luenberger, D. (1966). Observers for multivariable systems. IEEE Trans. Autom. Control 11: (2), 190–197.
 Thau, E.E. (1973). Observing the state of nonlinear systems. Int.J. Control 17: 471–479.
 Birk, J.; Zeitz, M. (1988). Extended-Luenberger observer for non-linear multivariable systems. Int. J. Control 47: (6), 1823–1836.
 Cox, H. (1964). On the estimation of state variables and parameters for noisy dynamic systems. IEEE Trans. Autom. Control 9: (1), 5–12.
 Drakunov, S.V. (1983). An adaptive quasioptimal filter with discontinuous parameters. Autom. Remote Control 44: (9), 1167–1175.
 Slotine, J. J.; Hedrick, J. K.; Misawa, E. A. (1987). On sliding observers for nonlinear systems. Journal of Dynamic Systems, Measurement, and Controlv109: (3), 245-252.
 Gauthier, J.P.; Hammouri, H.; Othman, S. (1992). A simple observer for nonlinear systems applications to bioreactors. IEEE Trans. Autom. Control 37: (6), 875–880.
 Tanaka, K.; Wang, H.O. (1997). Fuzzy regulators and fuzzy observers: a linear matrix inequality approach. In: Proceedings of the 36th IEEE Conference on Decision and Control (2) 1315–1320, San Diego, California.
 Beyhan, S. (2013). Runge–Kutta model-based nonlinear observer for synchronization and control of chaotic systems. ISA Trans. 52: (4), 501–509.
 Cetin, M.; Beyhan, S.; Iplikci, S. (2016). Soft sensor applications of RK-based nonlinear observers and experimental comparisons. Intelligent Automation & Soft Computing, DOI: 10.1080/10798587.2016.1147763
 İplikci, S. (2013). Runge–Kutta model-based adaptive predictive control mechanism for non-linear processes. Trans. Inst. Meas. Control 35: (2), 166–180.
 Butcher, J. C. (1987). The numerical analysis of ordinary differential equations: Runge-Kutta and general linear methods. Wiley-Interscience.
 Spurgeon, S.K. (2008). Sliding mode observers: a survey. Int. J. Syst.Sci.39: (8), 751–764.
 Hosani, Al.; Utkin, K. (2012). Parameters estimation using sliding mode observer with shift operator. J. Frankl. Inst. 349: (4),1509–1525.