Recurring discharge patterns in multiple spike trains. I. Detection.

We present a procedure to detect recurring discharge patterns in multiple spike trains. Such recurring patterns can include many spikes and involve from three to many spike trains. The pattern detection procedure is based on calculating the exact probability of randomly obtaining each individually recurring pattern. The statistical evaluation is based on the use of 2 x 2 contingency tables and the application of Fisher's exact test. Several simulations are applied to evaluate the method. Findings based on applying the procedure to simultaneously recorded spike and event trains are described in a companion paper (Frostig et al. 1990).

Analysis of frequency components in time series data.

We describe a method for assessing the periodic elements of variations in a time series and illustrate it with examples drawn from infant cardiac beat-to-beat intervals. Compared with averaging techniques, the procedure has the advantage of providing quantification of periodical elements in a non-stationary time series. Moreover, the procedure is robust to artifacts such as those which frequently contaminate beat-to-beat interval data. The method examines successive increments of the time-series plot, and when they become negative or positive, peaks and troughs are noted in the curve. Two successive troughs confine a wave which may be described by its amplitude and period. The set of all waves, terminated by the sequence of troughs, is defined as the "high-frequency component" of the series. Waves of the next low-frequency component are delineated when only the high-frequency peaks (or troughs) are considered. Thus, low-frequency peaks are defined as peaks of the curve formed by the high-frequency peaks, and lower-frequency troughs are the troughs of the curve formed by the high-frequency troughs. The process iterates to assess variations at lower and lower frequencies and any specific frequency component is being characterized by the median and interquartile range of its wave amplitudes and wave periods.

Information trains. The technique and its uses in spike train and network analysis, with examples taken from the nucleus parabrachialis medialis during sleep-waking states.

We describe an analytical procedure for assessing functional interactions between neuronal spike trains based on the outcome of cross-correlation procedures. Subsets of a reference cell spike train in a two-train recording are extracted, based on their time-locked relationship to spikes in the dependent train. Such timing relationships comprise the significant primary structures in the cross-correlogram. Different subsets can be extracted for different primary structures in the same correlogram (i.e. a subset responsible for an interaction effect, a subset responsible for a shared input effect, etc.). These new spike trains represent an information transfer process across synapses. These 'information trains' may be compared and correlated to different cells of the network across different functional conditions such as sleep-waking states, and may also be subjected to conventional spike train analysis techniques such as rate histogram, auto-correlation and cross-correlation procedures. We illustrate the information train procedures with a network analysis of a set of cells recorded in the nucleus parabrachialis medialis during different sleep-waking states.

Development of sleep-waking temporal sequencing in infants at risk for the Sudden Infant Death Syndrome.

The periodic organization of waking, quiet sleep, and active sleep was studied in control infants and siblings of victims of the Sudden Infant Death Syndrome. Spectral estimates of all-night binary state time series recorded at 1 week and 1, 2, 3, 4, and 6 months of age revealed disturbed patterns of sleep states, especially in active sleep, from as early as the first week of life. These disruptions continued until at least 6 months of age. These data support the contention that the temporal patterning of sleep state can be used as an important neurologic marker for development.