ArticleJournal of computational neuroscience2023
Adaptive unscented Kalman filter for neuronal state and parameter estimation.
Article in Journal of computational neuroscience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed.
- Current Status of AI-Assisted Screening for Opportunistic Osteoporosis.Current osteoporosis reports · 2026Review
- Parameter estimation of the network of FitzHugh-Nagumo neurons based on the speed-gradient and filtering.Journal of computational neuroscience · 2025Article
- A novel SVD-UKFNN algorithm for predicting current efficiency of aluminum electrolysis.Scientific reports · 2025Article
- Multivariate linear time-series modeling and prediction of cerebral physiologic signals: review of statistical models and implications for human signal analytics.Frontiers in network physiology · 2025Review
- Probabilistic Estimation and Control of Dynamical Systems Using Particle Filter with Adaptive Backward Sampling.Entropy (Basel, Switzerland) · 2024Article
- Reduced oriens-lacunosum/moleculare cell model identifies biophysical current balances forFrontiers in neural circuits · 2023Article
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Authors and funding
5 authors.
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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.
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