Roitman Data and Code

The related data and Matlab source code are made public available here for the following journal publication:

Roitman, JD and Shadlen, MN (2002), “Response of neurons in the lateral intraparietal area during a combined visual discrimination reaction time task”, Journal of Neuroscience, Vol. 22(21), 9475-9489.

The data collection was performed by Jamie D. Roitman and Michael N. Shadlen, and this research was supported by

• Grants EY-07031, EY-11378, RR-00166

• McKnight Foundation

• Howard Hughes Medical Institute

All data included in this zip package are in matlab .mat file form. The original data files are organized into four zip packages and each contains a list of .mat files for each neuron, which are

• all_b_fd.tar.gz for monkey B, Fixed Duration task

• all_b_rt.tar.gz for monkey B, Reaction Time task

• all_n_fd.tar.gz for monkey N, Fixed Duration task

• all_n_rt.tar.gz for monkey N, Reaction Time task

Most of the data is in multiple matlab files, each with a single large structure. To find out more about this structure, please see the example script sampleScript.m. This script creates several raster plots as an example of how to use this structure.

Data file T1RT.mat.gz includes one zipped file that is a compilation of all data used to create Fig. 7 of this paper. It is in an easier format, and all bad trials (ones with recording problems or ones where the subject did not complete the task) are already excluded. File T1RT.mat.gz includes several variables:

• x is the data matrix. Each row is a separate trial, and the columns are described in file ColumnNames608.m

• f is a column of file names. Each row corresponds to a trial from matrix x and shows from which data file the trial originally came

• s is a column of cells. Each cell corresponds to a trial from matrix x and has contained in it all of the spike times from that trial, beginning 100 ms before the stimulus onset and ending 200 ms after the saccade

Matlab code file lip_rt_roit_fig_7.m is the main mfile to plot Fig. 7 of this paper. This file takes the data from a rather raw form, and creates the PSTHs that are then plotted, so depending on the speed of your machine, this file may take quite a while to run. File lip_rt_roit_fig_7.m also depends on the following files to generate the plot:

makePSTH_excl100_200.m produces the PSTHs that the figure plots
ColumnNames608.m script associates column numbers and names, and gives some comments as to what is in each of the columns
nanrunmean.m computes the running mean of n values left and right of each value of z, respecting to nans
nans.m makes an array of nans, analogous to matlab functions ones and zeros