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- W2059663910 abstract "We demonstrate that a meaningful estimation of the neuronal network dynamics from single measured variable is now possible by incorporating biophysical neuronal models into a model-based control framework. Specifically, we assimilate noisy membrane potential (V) measurements from individual hippocampal neurons to reconstruct the dynamics of networks of these cells, their extracellular microenvironment, and the activities of different neuronal types during seizures. We used two-compartmental models for the pyramidal cells (PCs) and interneurons (INs): a cellular compartment and the surrounding extracellular microenvironment. The membrane potentials of both cells were modeled by Hodgkin-Huxley equations containing sodium, potassium, calcium-gated potassium, and leak currents. The current equations were augmented with dynamic variables representing the intra- and extracellular ion concentrations (K + , Na + ,a nd Ca 2+ ). To estimate and track the dynamics of the neuronal networks, we applied a nonlinear ensemble version of the Kalman filter, the Unscented Kalman Filter (UKF) [1]. Details of the model and UKF implementation can be found in [2,3]. In Fig. 1 we show an example where we use V measurements to estimate the rest of the dynamics of CA1 PC. Fig. 1A shows an intracellular recording from a PC during seizures, and plot the estimated [K]o in Fig. 1B." @default.
- W2059663910 created "2016-06-24" @default.
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- W2059663910 date "2010-07-01" @default.
- W2059663910 modified "2023-09-30" @default.
- W2059663910 title "Tracking neuronal dynamics during seizures" @default.
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- W2059663910 doi "https://doi.org/10.1186/1471-2202-11-s1-o9" @default.
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