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This study explores how uncertainty in time-varying parameter estimates obtained using nonlinear filtering algorithms with parameter tracking affects corresponding model output predictions. Results are demonstrated on a numerical example estimating the time-varying external voltage parameter in the FitzHugh-Nagumo system for modeling the spiking dynamics of neurons.
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Estimating Time-Varying Applied Current in the Hodgkin-Huxley Model
The classic Hodgkin-Huxley model is widely used for understanding the electrophysiological dynamics of a single neuron. While applying a low-amplitude constant current to the system results in a single voltage spike, it is possible to produce multiple voltage spikes by applying time-varying currents, which may not be experimentally measurable. The aim of this work is to estimate time-varying applied currents of different deterministic forms given noisy voltage data. In particular, we utilize an augmented ensemble Kalman filter with parameter tracking to estimate four different time-varying applied current parameters and associated Hodgkin-Huxley model states, along with uncertainty bounds in each case. We test the efficiency of the parameter tracking algorithm in this setting by analyzing the effects of changing the standard deviation of the parameter drift and the frequency of data available on the resulting time-varying applied current estimates and related uncertainty.
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- Award ID(s):
- 1819203
- PAR ID:
- 10166177
- Date Published:
- Journal Name:
- Applied Sciences
- Volume:
- 10
- Issue:
- 2
- ISSN:
- 2076-3417
- Page Range / eLocation ID:
- 550
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/app10020550
