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• W4296001847 abstract "Abstract Data assimilation techniques for state and parameter estimation are frequently applied in the context of computational neuroscience. In this work, we show how an adaptive variant of the unscented Kalman filter (UKF) performs on the tracking of a conductance-based neuron model. Unlike standard recursive filter implementations, the robust adaptive unscented Kalman filter (RAUKF) jointly estimates the states and parameters of the neuronal model while adjusting noise covariance matrices online based on innovation and residual information. We benchmark the adaptive filter's performance against existing nonlinear Kalman filters and explore the sensitivity of the filter parameters to the system being modelled. To evaluate the robustness of the proposed solution, we simulate practical settings that challenge tracking performance, such as a model mismatch and measurement faults. Compared to standard variants of the Kalman filter the adaptive variant implemented here is more accurate and robust to faults." @default.
• W4296001847 created "2022-09-16" @default.
• W4296001847 creator A5004725083 @default.
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• W4296001847 date "2022-09-07" @default.
• W4296001847 modified "2023-10-18" @default.
• W4296001847 title "Adaptive Unscented Kalman Filter for Neuronal State and Parameter Estimation" @default.
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• W4296001847 doi "https://doi.org/10.21203/rs.3.rs-2011398/v1" @default.
• W4296001847 hasPublicationYear "2022" @default.
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