Abstract
The circadian pacemaker of the SCN is a heterogeneous structure containing many single-cell oscillators that display phase differences in gene expression and electrical activity rhythms. Thus far, it is unknown how single neurons contribute to the population signal measured from the SCN. The authors used single-unit electrical activity rhythms that have previously been recorded in SCN slices and investigated in simulation studies how changes in pattern shape and distribution of single neurons alter the ensemble activity rhythm of the SCN. The results were compared with recorded ensemble rhythms. The simulations show that single units should be distributed in phase to render the recorded multiunit waveform and that different distributions can account for the multiunit pattern of the SCN, including a bimodal distribution. Vice versa, the authors show that the single-unit distribution cannot be inferred from the ensemble pattern. Photoperiodic encoding by the SCN relies on changes in waveform of the neuronal output from the SCN and received special attention in this study’s simulations. The authors show that a broadening or narrowing of the multiunit pattern can be based on changes in phase differences between neurons, as well as on changes in the circadian pattern of individual neurons. However, these mechanisms give rise to differences in the maximal discharge level of the multiunit pattern, leading to testable predictions to distinguish between the 2 mechanisms. If single units broaden their activity pattern in long days, the maximum frequency of the multiunit activity should increase, while an increase in phase difference between the single-unit activity rhythms should lead to a decrement in maximum frequency. The simulations also show that coding for day-length by an evening and morning oscillator is not self-evident and will only work under a limited set of conditions in which the distribution within each component and temporal distance between the components is taken into account. While the simulations were based on single-cell and multiunit electrical activity patterns, they are also relevant for understanding the relation between single-cell and population molecular expression profiles.