THE ALGORITHMIC COMPLEXITY OF NEURAL SPIKE TRAINS INCREASES DURING FOCAL SEIZURES

The interspike interval spike trains of spontaneously active cortical neurons can display nonrandom internal structure. The degree of nonran dom structure can be quantified and was found to decrease during focal epileptic seizures. Greater statistical discrimination between the tw o physiological conditions (normal vs seizure) was obtained with,measu rements of context-free grammar complexity than by measures of the dis tribution of the interspike intervals such as the mean interval, its s tandard deviation, skewness, or kurtosis. An examination of fixed epoc h data sets showed that two factors contribute to the complexity: the firing rate and the internal structure of the spike train. However, ca lculations with randomly shuffled surrogates of the original data sets showed that the complexity is not completely determined by the firing rate. The sequence-sensitive structure of the spike train is a signif icant contributor. By combining complexity measurements with statistic ally related surrogate data sets, it is possible to classify neurons a ccording to the dynamical structure of their spike trains. This classi fication could not have been made on the basis of conventional distrib ution-determined measures. Computations with more sophisticated kinds of surrogate data show that the structure observed using complexity me asures cannot be attributed to linearly correlated noise or to linearl y correlated noise transformed by a static monotonic nonlinearity. The patterns in spike trains appear to reflect genuine nonlinear structur e. The limitations of these results are also discussed. The results pr esented in this article do not, of themselves, establish the presence of a fine-structure encoding of neural information.