Adaptive unscented Kalman filter for neuronal state and parameter estimation

Bano-Otalora, B., Moye, M. J., Brown, T., Lucas, R. J., Diekman, C. O., & Belle, M. D. (2021). Daily electrical activity in the master circadian clock of a diurnal mammal. eLife, 10, e68179. Publisher: eLife Sciences Publications, Ltd.

Barfoot, T. D. (2017). State estimation for robotics. Cambridge University Press.

Book  Google Scholar 

Berry, T., & Sauer, T. (2013). Adaptive ensemble kalman filtering of non-linear systems. Tellus A: Dynamic Meteorology and Oceanography, 65, 20331.

Article  Google Scholar 

Destexhe, A., Rudolph, M., Fellous, J. M., & Sejnowski, T. J. (2001). Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons. Neuroscience, 107, 13–24.

Article  CAS  PubMed  Google Scholar 

Golowasch, J. (2014). Ionic current variability and functional stability in the nervous system. BioScience, 64, 570–580.

Article  PubMed  PubMed Central  Google Scholar 

Hajiyev, C., & Caliskan, F. (2003). Fault diagnosis and reconfiguration in flight control systems. Boston: Kluwer Academic Publishers.

Hajiyev, C., & Soken, H. E. (2014). Robust adaptive unscented kalman filter for attitude estimation of pico satellites. International Journal of Adaptive Control and Signal Processing, 28, 107–120.

Article  Google Scholar 

Hamilton, F., Berry, T., & Sauer, T. (2018). Tracking intracellular dynamics through extracellular measurements. PLoS One, 13(10), e0205031

Article  Google Scholar 

Hilscher, M. M., Nogueira, I., Mikulovic, S., Kullander, K., Leão, R. N., & Leão, K. E. (2019). Chrna2-olm interneurons display different membrane properties and h-current magnitude depending on dorsoventral location. Hippocampus, 29, 1224–1237.

Article  CAS  PubMed  Google Scholar 

Julier, S., & Uhlmann, J. (1997). New extension of the Kalman filter to nonlinear systems. In Kadar, I. (ed.), Signal Processing, Sensor Fusion, and Target Recognition VI (vol. 3068). International Society for Optics and Photonics SPIE. pp. 182–193.

Julier, S., Uhlmann, J., & Durrant-Whyte, H. (2000). A new method for the nonlinear transformation of means and covariances in filters and estimators. IEEE Transactions on Automatic Control, 45, 477–482.

Article  Google Scholar 

Kadakia, N. (2022). Optimal control methods for nonlinear parameter estimation in biophysical neuron models. PLOS Computational Biology, 18, e1010479.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Kalman, R. E. (1960). A new approach to linear filtering and prediction problems. Journal of Basic Engineering, 82, 35–45.

Article  Google Scholar 

Klausberger, T., & Somogyi, P. (2008). Neuronal diversity and temporal dynamics: The unity of hippocampal circuit operations. Science, 321, 53–57.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Lankarany, M., Heiss, J. E., Lampl, I., & Toyoizumi, T. (2016). Simultaneous Bayesian estimation of excitatory and inhibitory synaptic conductances by exploiting multiple recorded trials. Frontiers in Computational Neuroscience, 10.

Lankarany, M., Zhu, W. -P., & Swamy, M. (2014). Joint estimation of states and parameters of hodgkin-huxley neuronal model using kalman filtering. Neurocomputing, 136, 289–299.

Article  Google Scholar 

Lankarany, M., Zhu, W. -P., Swamy, M. N. S., & Toyoizumi, T. (2013). Inferring trial-to-trial excitatory and inhibitory synaptic inputs from membrane potential using gaussian mixture Kalman filtering. Frontiers in Computational Neuroscience, 7.

Mohamed, A. H., & Schwarz, K. P. (1999). Adaptive kalman filtering for ins/gps. Journal of Geodesy, 73, 193–203.

Article  Google Scholar 

Morris, C., & Lecar, H. (1981). Voltage oscillations in the barnacle giant muscle fiber. Biophysical journal, 35, 193–213.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Moye, M. J., & Diekman, C. O. (2018). Data assimilation methods for neuronal state and parameter estimation. The Journal of Mathematical Neuroscience, 8, 11.

Article  PubMed  Google Scholar 

Prescott, S. A., De Koninck, Y., & Sejnowski, T. J. (2008). Biophysical basis for three distinct dynamical mechanisms of action potential initiation. PLOS Computational Biology, 4, 1–18.

Article  Google Scholar 

Schiff, S. J. (2009). Kalman meets neuron: the emerging intersection of control theory with neuroscience. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2009, 3318–3321.

Google Scholar 

Schiff, S. J. (2011). Neural control engineering: The emerging intersection between control theory and neuroscience.

Sekulić, V., Yi, F., Garrett, T., Guet-McCreight, A., Lawrence, J. J., & Skinner, F. K. (2020). Integration of within-cell experimental data with multi-compartmental modeling predicts h-channel densities and distributions in hippocampal OLM cells. Frontiers in Cellular Neuroscience, 14.

Simon, D. (2010). Kalman filtering with state constraints: A survey of linear and nonlinear algorithms. IET Control Theory and Applications, 4, 1303–1318.

Article  Google Scholar 

Skinner, F. (2006). Conductance-based models. Scholarpedia, 1, 1408.

Article  Google Scholar 

Stengel, R. F. (1994). Optimal control and estimation. Dover Publications.

Sun, Z., Crompton, D., Lankarany, M., & Skinner, F. K. (2022). Reduced oriens-lacunosum/moleculare (OLM) cell model identifies biophysical current balances for in vivo greater theta frequency spiking resonance. bioRxiv. Retrieved from: https://doi.org/10.1101/2F2022.10.20.513073

Toth, B. A., Kostuk, M., Meliza, C. D., Margoliash, D., & Abarbanel, H. D. I. (2011). Dynamical estimation of neuron and network properties i: Variational methods. Biological Cybernetics, 105, 217–237.

Article  PubMed  PubMed Central  Google Scholar 

Ullah, G., & Schiff, S. J. (2009). Tracking and control of neuronal hodgkin-huxley dynamics. Physical Review E, 79,

Article  Google Scholar 

Voss, H., Timmer, J., & Kurths, J. (2004). Nonlinear dynamical system identification from uncertain and indirect measurements. International Journal of Bifurcation and Chaos, 14, 1905–1933.

Article  Google Scholar 

Zheng, B., Fu, P., Li, B., & Yuan, X. (2018). A robust adaptive unscented Kalman filter for nonlinear estimation with uncertain noise covariance. Sensors, 18, 808.

Article  PubMed  PubMed Central  Google Scholar 

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