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HyungGoo Kim
Aug 15, 2022
5be0dd1 · Aug 15, 2022

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libtimeseries

libtimeseries provides an integrative way to plot and manage behavioral and neural time course data. It is specialized in handling trial structure with multiple conditions and differen events.

see demo_timecourse_data_analysis.m for a demo with detailed comments.

What you can do

  • plot a PSTH (show raster + average across trials for each condition)
  • save a PSTH
  • load multiple PSTHs
  • filter PSTHs
  • combine PSTHs with different time range (e.g., homogenize x axis of PSTHs)
  • plot individual or population PSTHs
  • perform multi-dimensional population analyses

Examples

Sinle-session time courses

Fig1 Fig2

Population time courses: inspect individuals and plot the average

xsession mpsths

Details

psth struct

plot_timecourse returns a struct variable that contains essential information about the time course plot. The table below describes select fields of the psth struct.

name size description
x [1 * # of timepoints] time point of PSTHs
mean [# of groups * # of timepoints] average activity for each group
sem [# of groups * # of timepoints] standard error of mean for each group
std [# of groups * # of timepoints] standard deviation for each group
numel [# of groups * # of timepoints] # of valid trials for each group
pDiff [1 * # of timepoints] p-values to test whether responses are same or different across groups
pBaseDiff [# of groups * # of timepoints] p-values to test whether responses are same or different from baseline
event [1 * # of events] a table containing the medians of events
rate_rsp [# of trials * # of timepoints] trial-to-trial responses that was used to compute the averages for each group
ginfo.grp_idx [# of trials * 1] group indice of individual trials
ginfo.unq_grp_label [# of groups * 1] string labels for the group indice