Estimating Time-Varying Applied Current in the Hodgkin-Huxley Model

TL;DR

This paper develops an augmented ensemble Kalman filter approach to estimate unknown time-varying applied currents in the Hodgkin-Huxley neuron model from noisy voltage data, enabling better understanding of neuronal dynamics.

Contribution

It introduces a novel application of parameter tracking with ensemble Kalman filtering to estimate deterministic time-varying currents in neuron models.

Findings

Effective estimation of different deterministic currents from noisy data.

Influence of parameter drift and data frequency on estimation accuracy.

Enhanced understanding of neuron response to time-varying stimuli.

Abstract

The classic Hodgkin-Huxley model is widely used for understanding the electrophysiological dynamics of a single neuron. While applying a constant current to the system results in a single voltage spike, it is possible to produce more interesting dynamics 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 deterministic applied currents, analyzing how the model dynamics change in each case. We test the efficiency of the parameter tracking algorithm in this setting by exploring 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.