Spike Train Analysis Toolkit Documentation  Spike Train Analysis Toolkit Function Reference  directformal

Spike Train Analysis Toolkit

directformal

DIRECTFORMAL Direct method analysis to determine formal information.
   Y = DIRECTFORMAL(X_UNI,X_REP,OPTS) uses the direct method to
   find the formal information. X_UNI is an input data structure
   with spike trains obtained from distinct stimuli and is used to
   estimate the "total" entropy. Each spike train is considered to
   be in its own stimulus category. X_REP is an input data
   structure with spike trains obtained from repitition of the
   same stimulus. The results are stored in the structure Y.

   The members of Y are:
      Y.uni_binned: Binned versions of the spike trains in
         X_UNI. See DIRECTBIN for details. 
      Y.rep_binned: Binned versions of the spike trains in
         X_REP. See DIRECTBIN for details.
      Y.cond: A HISTCOND structure with word counts for total and
         class-conditional histograms. See DIRECTCOUNTCOND and
         INFOCOND for details. 

   The options and parameters for this function are:
      OPTS.start_time: The start time of the analysis window. The
         default is the maximum of all of the start times in X. 
      OPTS.end_time: The end time of the analysis window. The
         default is the minimum of all of the end times in X.
      OPTS.counting_bin_size: The size of the counting bins in
         seconds. The default is OPTS.end_time-OPTS.start_time.
      OPTS.words_per_train: The number of words that the spike trains
         will be divided into.
      OPTS.sum_spike_trains: For data sets with simultaneously
         recorded spike trains, this determines whether the
         simultaneous spikes are summed across time bins.
         OPTS.sum_spike_trains=0 means there is no summing.
         OPTS.sum_spike_trains=1 means there is summing.
         The default value is 0.
      OPTS.permute_spike_trains: For data sets with simultaneously
         recorded spike trains, this determines whether sets of
         simultaneous spike trains that are permuted should be
         considered identical.
         OPTS.permute_spike_trains=0 means they are considered
            distinct.
         OPTS.permute_spike_trains=1 means they are considered
            indentical.
         The default value is 0.
      OPTS.legacy_binning: Allows binning to be done in a manner
         compatible with version 1.1 and earlier of the toolkit.
         These older versions created an extra (empty) bin when
         (OPTS.end_time-OPTS.start_time) is an integer multiple of
         (OPTS.words_per_train*OPTS.counting_bin_size).
         OPTS.legacy_binning=0 means use the current binning method.
         OPTS.legacy_binning=1 means use the legacy binning method.
         The default value is 0.
      OPTS.letter_cap: Places a cap on the maximum number of spikes
         to be counted in a bin. The default value is Inf.

   Y = DIRECTFORMAL(X_UNI,X_REP) uses the default options and parameters.

   [Y,OPTS_USED] = DIRECTFORMAL(X_UNI,X_REP) or [Y,OPTS_USED] =
   DIRECTFORMAL(X_UNI,X_REP,OPTS) additionally return the options
   used.

   See also DIRECTCAT, DIRECTBIN, DIRECTCONDTIME,
   DIRECTCONDFORMAL, DIRECTCOUNTCLASS, DIRECTCOUNTTOTAL.

Cross-reference information

This function calls:

This function is called by:


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