synthesis.dapp: Simulate Multiplexing Data for DAPP Analysis
In neuromplex: Neural Multiplexing Analysis

Description Usage Arguments Value Examples

View source: R/dynamic_neural_model-v6.R

Simulate spike trains from controlled DAPP setting with flat and sinusoidal weight curves

 
synthesis.dapp(ntrials = c(10, 10, 10), time.bins = 0:1000, lambda.A = 400,
     lambda.B = 100, pr.flat = 0.5, intervals = list(c(0,1)),
     wts = 1, span = c(0,1), period.range = c(400, 1000))

`ntrials`	a vector of 3 elements giving the trial counts for conditions A, B and AB
`time.bins`	time bins (in ms) giving the break points of the time bins in which Poisson draws should be made to mimic a Poisson process generation
`lambda.A`	a flat intensity (in Hz) for condition A
`lambda.B`	a flat intensity (in Hz) for condition B
`pr.flat`	proportion of flat weight curves to be generated
`intervals`	a list of sub-intervals (each represented by the 2-vector giving the sub-interval end-points) which determine the ranges of the flat weight curves
`wts`	the relative weights of the sub-intervals above
`span`	a two-vector giving the range of the sinusoidal weight curves
`period.range`	the range from which the sinusoidal periods are drawn randomly (and uniformly)

Returns a list containting the following items.

`spiketimes`	a list with 3 elements giving the 3 sets of spiketimes associated with experimental conditions A, B and AB
`alphas`	true underlying weight curves for each AB trial
`lambdas`	corresponding intensity curves for each AB trial
`time.pts`	time points associated with alphas and lambdas

## generate 25 A and 30 B trials with rate functions
##    lambda.A(t) = 160*exp(-2*t/1000) + 40*exp(-0.2*t/1000)
##    lambda.B(t) = 40*exp(-2*t/1000)
## where time t is measured in ms. Then, generate 40 AB trials,
## roughly half with flat weight curves with a constant intensity
## either close to A, or close to B or close to the 50-50 mark,
## (equally likely). The remaining curves are sinusoidal
## that snake between 0.01 and 0.99 with a period randomly
## drawn between 400 and 1000

ntrials <- c(nA=25, nB=30, nAB=40)
flat.range <- list(A=c(0.85, 0.95),
                   B=c(0.05, 0.15),
                   mid=c(0.45,0.55))
flat.mix <- c(A=1/3, B=1/3, mid=1/3)
wavy.span <- c(0.01, 0.99)
wavy.period <- c(400, 1000)

T.horiz <- 1000
rateB <- 40 * exp(-2*(1:T.horiz)/T.horiz)
rateA <- 4*rateB + 40 * exp(-0.2*(1:T.horiz)/T.horiz)

synth.data <- synthesis.dapp(ntrials = ntrials, pr.flat = 0.5,
                             intervals = flat.range, wts = flat.mix,
                             span = wavy.span, period.range = wavy.period,
                             lambda.A=rateA, lambda.B=rateB)

## Visualize data and generate binned spike counts
spike.counts <- mplex.preprocess(synth.data$spiketimes, visualize=TRUE, top="Synthetic Data")