neuronal.data: Trajectories Interspike Of A Single Neuron Of A Ginea Pig

In MsdeParEst: Parametric Estimation in Mixed-Effects Stochastic Differential Equations

Description

The neuronal.data data has 240 measurements of the membrane potential in volts for one single neuron of a pig between the spikes, along time, with 2000 points for each. The step time is delta= 0.00015 s.

Usage

  neuronal.data

Format

This data frame has a list form of length 2. The first element in the matrix named Xreal. Each row is a trajectory, that one can model by a diffusion process with random effect. The realisation can be assumed independent. The second element is a vector of times of observations times

Source

The parameters of the stochastic leaky integrate-and-fire neuronal model. Lansky, P., Sanda, P. and He, J. (2006). Journal of Computational Neuroscience Vol 21, 211–223

References

The parameters of the stochastic leaky integrate-and-fire neuronal model. Lansky, P., Sanda, P. and He, J. (2006). Journal of Computational Neuroscience Vol 21, 211–223

Examples

M <- 240     # number of trajectories, number of rows of the matrix of the data
T <- 0.3     # width of the interval of observation 
delta <- 0.00015   # step time
N <- T/delta  # number of points in the time interval 2000

data(neuronal.data)
# reduction of data for example to save running times
ind <- seq(1, 2000, by = 20)
X <- neuronal.data[[1]][1:50, ind]
times <- neuronal.data[[2]][ind]

# - 1) Ornstein-Uhlenbeck with two random effects in the drift and one fixed effect in the diffusion

estim<- msde.fit(times=times, X=X, model="OU")
# summary(estim)

## Not run: 
# - 2) Cox-Ingersoll-Ross with one random effect in the drift and one random effect in the diffusion

estim<- msde.fit(times=times, X=X, model="CIR", drift.random=1, diffusion.random=1)
# summary(estim)

# - 3) Cox-Ingersoll-Ross with one random effect in the drift and one fixed effect in the 
# diffusion  

estim<- msde.fit(times=times, X=X, model="CIR", drift.random=1)
# summary(estim)

# - 4) Ornstein-Uhlenbeck with a mixture distribution for the two random effects in the drift
# and one fixed effect in the diffusion

estim<- msde.fit(times=times, X=X, model="OU", nb.mixt=2)
summary(estim)

## End(Not run)

MsdeParEst documentation built on May 2, 2019, 3:01 a.m.