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Methods for estimating spike train information rates

The expression given in Eq. (5) for the mutual information is in practice difficult to evaluate because estimating the distributions tex2html_wrap_inline1255, tex2html_wrap_inline1257 and tex2html_wrap_inline1259 may require very large amounts of data. For example, suppose that there are 1000 input spike trains driving the output, and that each spike train is divided into segments 100 msec in length, and discretized into 1 msec bins. There are then tex2html_wrap_inline1285 possible output spike trains, tex2html_wrap_inline1287 sets of input spike trains, and tex2html_wrap_inline1289 possible combinations of input and output spike trains forming the space over which the joint distribution tex2html_wrap_inline1259 must be estimated. While this naive calculation is in practice an overestimate (see [de Ruyter van Steveninck et al., 1997, Buracas et al., 1996] for methods that make use of the fact that most spike trains are very unlikely), it emphasizes the potential problems involved in estimating the mutual information. Below we describe two practical methods for computing information rates.





Tony Zador
Fri Nov 28 10:17:14 PST 1997