title: Real-time tracking of neuronal network structure using data assimilation.
abstract: A nonlinear data assimilation technique is applied to determine and track effective connections between ensembles of cultured spinal cord neurons measured with multielectrode arrays. The method is statistical, depending only on confidence intervals, and requiring no form of arbitrary thresholding. In addition, the method updates connection strengths sequentially, enabling real-time tracking of nonstationary networks. The ensemble Kalman filter is used with a generic spiking neuron model to estimate connection strengths as well as other system parameters to deal with model mismatch. The method is validated on noisy synthetic data from Hodgkin-Huxley model neurons before being used to find network connections in the neural culture recordings.
authors: Electrical and Computer Engineering, George Mason University, Fairfax, Virginia 22030, USA.
authors: Franz
authors: F
authors: Hamilton
authors: Mathematical Sciences, George Mason University, Fairfax, Virginia 22030, USA.
authors: Tyrus
authors: T
authors: Berry
authors: Electrical and Computer Engineering, George Mason University, Fairfax, Virginia 22030, USA.
authors: Nathalia
authors: N
authors: Peixoto
authors: Mathematical Sciences, George Mason University, Fairfax, Virginia 22030, USA.
authors: Timothy
authors: T
authors: Sauer
mesh_terms: D000200:Action Potentials; D008839:Microelectrodes; D008959:Models, Neurological; D009415:Nerve Net / Q000166:cytology* / Q000502:physiology*; D009474:Neurons / Q000166:cytology*; D013997:Time Factors