Nonlinear analysis of interspike intervals.

Daniel M. Racicot
Theories of neural coding and neural information processing rely on a knowledge of the correlations between firing events. We present a method of analyzing experimental interspike interval (ISI) sequences from neurons for the presence of nonlinear correlations. This is accomplished by comparing nonlinear predictions on experimental data sets and on their "surrogate" data sets. These surrogates have the same linear properties as the experimental data, but are otherwise stochastic. A difference in nonlinear predictability between...
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