FNdat: Dataset of noisy observations from the FitzHugh-Nagumo (FN)...

In magi: MAnifold-Constrained Gaussian Process Inference

Dataset of noisy observations from the FitzHugh-Nagumo (FN) equations

Description

The classic FN equations model the spike potentials of neurons, where system components V and R are the voltage and recovery variables, respectively.

V and R are governed by the following differential equations:

\frac{dV}{dt} = c(V-\frac{V^3}{3}+R)

\frac{dR}{dt} = -\frac{1}{c}(V-a+bR)

where \theta = (a,b,c) are system parameters. This dataset was generated by first numerically solving these ODEs from t=0 to t=20, with initial conditions V(0) = -1 and R(0) = 1 and parameters \theta = (0.2, 0.2, 3). The system components were taken to be measured at 28 observation time points (as indicated in time column) with additive Gaussian noise (standard deviation 0.2).

Usage

data(FNdat)

Format

A data frame with 28 rows and 3 columns (time, V, R).

References

FitzHugh, R (1961). Impulses and Physiological States in Theoretical Models of Nerve Membrane. Biophysical Journal, 1(6), 445–466.

magi documentation built on April 26, 2023, 1:12 a.m.