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Spike Train Analysis Toolkit

binlessinfo

BINLESSINFO Compute information components using binless method. 
   [I_PART,I_CONT,I_COUNT,I_TOTAL] =
   BINLESSINFO(X,COUNTS,CATEGORIES,M,OPTS) computes the various
   components of information in the matrix of embedded data X. COUNTS
   is a vector of the number of points in the data from which X is
   derived (e.g., spike counts), and CATEGORIES is a vector of
   category indices, as obtained from BINLESSOPEN. M is the number of
   categories.

   I_PART is the information conveyed by zero-distance data and
   singletons. I_CONT is the continuous component of the information
   which describes the separability of the embedded data. I_PART and
   I_CONT sum to give the "timing" component of the information.
   I_COUNT is the information conveyed by the number of points in the
   data. I_TOTAL is the sum of all of the components. While I_CONT is
   a scalar, I_PART, I_COUNT, and I_TOTAL are structures of type
   ESTIMATE.

      OPTS.min_embed_dim: The minimal embedding dimension for
         episodic data. The default is 1. (Related option
         OPTS.max_embed_dim is used by BINLESSEMBED.)
      OPTS.cont_min_embed_dim: The minimal embedding dimension for
         continuous data. The default is 0. (Related option
         OPTS.cont_max_embed_dim is used by BINLESSEMBED.)
      OPTS.stratification_strategy: The strategy for stratifying data
         by the number of points.
         OPTS.stratification_strategy=0 puts all data in a single
            stratum. 
         OPTS.stratification_strategy=1 stratifies data by the number
            of points. For continuous data in which the number of
            samples on each trial is the same (as is typical), this
            is equivalent to OPTS.stratification_strategy=0. For
            episodic data, each spike count gets its own stratum. 
         OPTS.stratification_strategy=2 is similar to option 1
            except that all data with more than
            OPTS.embed_dim_max-OPTS.embed_dim_min points go into a
            single stratum.
         The default value is 2 for episodic data and 0 for
         continuous data.
      OPTS.singleton_strategy: The strategy for handling
         singletons.
         OPTS.singleton_strategy=0 means that singletons are
            considered uninformative and are ignored. 
         OPTS.singleton_strategy=1 means that singletons are
            considered maximally informative and are included.
         The default value is 0.

   [I_PART,I_CONT,I_COUNT,I_TOTAL] =
   BINLESSINFO(X,COUNTS,CATEGORIES,M) uses the default options and
   parameters.

   [I_PART,I_CONT,I_COUNT,I_TOTAL,OPTS_USED] =
   BINLESSINFO(X,COUNTS,CATEGORIES,M) or
   [I_PART,I_CONT,I_COUNT,I_TOTAL,OPTS_USED] =
   BINLESSINFO(X,COUNTS,CATEGORIES,M,OPTS) additionally return the
   options used.
 
   See also BINLESSOPEN, BINLESSEMBED, BINLESSWARP.

Cross-reference information

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