neuromplex (version 1.0-1)

dapp: Dynamic Admixture of Poisson Process

Description

Fits the DAPP model to binned spiking data

Usage

dapp(spike.counts, lengthScale = NULL, lsPrior = NULL,
     hyper = list(prec = c(1,1), sig0 = 1.87, w=c(1,1)),
     burnIn = 1e3, nsamp = 1e3, thin = 4,
     verbose = TRUE, remove.zeros = FALSE)

Arguments

spike.counts

A list with the following items. 'Acounts': binned spike counts under condition A presented as a matrix. Rows are bins, columns are replicates (trials). 'Bcount': binned spike counts under condition B. 'ABcounts': binned spike counts under condition AB. 'bin.mids': an array giving the mid-points of the time bins. 'bin.width': a scalar giving the bin width.

lengthScale

an array giving the length scale parameter values to be used for Gaussian process prior. Defaults to sort(0.16 * resp.horiz / c(4, 3, 2, 1, 0.5, 0.1)) where resp.horiz is the time horizon of the response period.

lsPrior

an array of the same length as lengthScale giving the prior probabilities of the length scale values.

hyper

a list of hyper parameters with the following iterms. 'prec': a 2-vector giving the shape and rate parameters of the gamma distribution on the Dirichlet precision parameter. 'sig0': a scalaer giving the scale of the (centered) logistic distribution used in transforming the Gaussian random curves into curves restricted between 0 and 1.

burnIn

number of MCMC iterations to discard as burn-in.

nsamp

number of MCMC draws to be saved for posterior inference.

thin

the thinning rate at which MCMC draws are to be saved. The total number of iterations equals burnIn + nsamp * thin

verbose

logical indicating if some fit details should be printed during the course of the MCMC

remove.zeros

logical indicating if trials with zero spike count shuold be removed from the analysis

Value

Returns a list of class "dapp" containting the following items.

lsProb

posterior preditctive draws of length scale

lambda.A

posterior draws of lambda.A at bin mid-points

lambda.B

posterior draws of lambda.B at bin mid-points

alpha

posterior draws of the alpha curves at bin mid-points

A

posterior draws of the latent variable A which gives the AB spike counts (by bin) that are to be attributed to signal A (the remaining are attributed to signal B)

prec

posterior draws of precision

alpha.pred

posterior predictive draws of alpha (of a future trial)

psl.pred

posterior predictive draw of the feature parameters (phi, psi, ell) (of a future trial)

details

mcmc details given as an array of c(niter, nsamp, burnIn, thin, MH acceptance rate)

hyper

hyper parameters used in model fitting

lengthScale

length scale set used in model fitting

lsPrior

length scale prior

bin.mids

bin mid-points

bin.width

bin width

mcmc

mcmc controls (burn-in length, thinning rate and number of saved draws)

References

Glynn, C., Tokdar, S.T., Zaman, A., Caruso, V.C., Mohl, J.T., Willett, S.M., and Groh, J.M. (2020+). Analyzing second order stochasticity of neural spiking under stimuli-bundle exposure. The Annals of Applied Statistics. Accepted.

See Also

plot.dapp, summary.dapp and predict.dapp.

Examples

# NOT RUN {
  ## Note:
  #### The example below uses a simpler synthetic data, a wider bin-width
  #### and a shorter MCMC run to keep the run length less than 5s
  #### Use ?plot.dapp or ?plot.summary for a more realistic example
  
  ## Generate 30 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 25 AB trials,
  ## roughly 2/3 with flat weight curves with a constant intensity
  ## either close to A, or close to B (equally likely). The 
  ## remaining 1/3 curves are sinusoidal that snake between 0.01 and 0.99 
  ## with a period randomly drawn between 400 and 1000
  
  ntrials <- c(nA=30, nB=30, nAB=25)
  flat.range <- list(A=c(0.85, 0.95),
                     B=c(0.05, 0.15))
  flat.mix <- c(A=1/2, B=1/2)
  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 = 2/3,
                               intervals = flat.range, wts = flat.mix,
                               span = wavy.span, period.range = wavy.period,
                               lambda.A=rateA, lambda.B=rateB)
  
  ## Generate binned spike counts witb 100 ms bins
  spike.counts <- mplex.preprocess(synth.data$spiketimes, bw=100, visualize=FALSE)
  
  ## Fit the DAPP model to data
  #### A short MCMC run is done below to keep the run length short.
  #### Use default or larger values for burn, nsamp and thin
  #### for more reliable estimation
  fit.post <- dapp(spike.counts, burn=10, nsamp=90, thin=1, verbose=FALSE)
  
  ## Visualize model fit
  plot(fit.post)
  
  ## Post process results to assign second order stochasticity labels
  summary(fit.post)
# }