Neuronal firing patterns in the feline hippocampus during sleep and wakefulness

The study addressed the problem of information transmission in mammalian brain as reflected in the emergence or disappearance of temporal patterns in extracellularly monitored single action potentials from the dorsal hippocampus of unrestrained cats during slow wave sleep (SWS), rapid eye movement sleep (REM), and motionless quiet wakefulness (QW). The spike trains were analyzed with a nonparametric technique. Chi-square statistics were used to measure deviation of firing patterns from the theoretical model which is based on the assumption that the intervals are random and/or independent from each other. The plots of the chi-square values for a given set of patterns represented the neuronal 'signatures' characteristic of a behavioral state. During SWS most neurons followed the theoretical model, i.e. their 'signatures' were flat and statistically non-significant. However, during REM sleep and QW their firing modes showed specific deviations from the theoretical model: some patterns occurred more often while others less often than expected, thus generating large and statistically significant 'signatures'. During REM sleep some neurons shared similar tendencies in their departures from the theoretical model. However, during QW the same neurons developed their individual 'signatures' which were significantly different from each other. Hence, the QW episodes were characterized by a greater differentiation of neuronal firing patterns. The mean firing rate and the shape of the time interval histogram were not necessarily correlated with the emergence of specific temporal patterns in spike trains. The results suggest that information transmission from one neuron to another depends on the emergence of repetitive and specific temporal patterns. The strong tendency of most neurons to lapse during SWS into a firing mode that closely follows the theoretical model constitutes the basis for a working hypothesis which states that the essence of SWS recovery in cognitive systems is the disappearance of temporal patterns, and that the 'noisy' interactions between neurons plays an important role in the recuperative processes.