Data Assimilation Methods for Conductance-Based Neuronal Modeling

Casey Diekman (May 5, 2020)

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Abstract

Modern data assimilation (DA) techniques are widely used in climate science and weather prediction but have only recently begun to be applied in neuroscience. In this talk I will illustrate the use of DA algorithms to estimate unobserved variables and unknown parameters of conductance-based neuronal models and propose the bifurcation structure of inferred models as a qualitative measure of estimation success. I will then apply DA to electrophysiological recordings from suprachiasmatic nucleus neurons to develop models that provide insight into the functioning of the mammalian circadian clock. Finally, I will frame the selection of stimulus waveforms to inject into neurons during patch-clamp recordings as an optimal experimental design problem and present preliminary results on the optimal stimulus waveforms for improving the identifiability of parameters for a Hodgkin-Huxley-type model.