It is often of interest experimentally to assess how synchronization between two neurons changes following a stimulus or other behaviorally relevant marker. The joint peristimulus time histogram (JPSTH) achieves this, but assumes that changes in the cells' firing rate following the stimulus are stereotyped from one sweep to the next. Erroneous results can be generated if this is not the case. We here present a method to assess whether there are variations in response latency or amplitude from sweep to sweep. We then describe how the effects of response latency variation can be mitigated by realigning sweeps to their individual latencies. Three methods of detecting response latency are presented and their performance compared on simulated data. Finally, the effect on the JPSTH of sweep realignment using detected latencies is illustrated.

We outline two improvements to the technique of gravitational clustering for detection of neuronal synchrony, which are capable of improving the method's detection of weak synchrony with limited data. The advantages of the enhancements are illustrated using data with known levels of synchrony and different interspike interval distributions. The novel simulation method described can easily generate such test data. An important dependence of the sensitivity of gravitational clustering to the interspike interval distribution of the analysed spike trains is described.

Whether cortical neurons act as coincidence detectors or temporal integrators has implications for the way in which the cortex encodes information—by average firing rate or by precise timing of action potentials. In this study, we examine temporal coding by a simple passive-membrane model neuron responding to a full spectrum of multisynaptic input patterns, from highly coincident to temporally dispersed. The temporal precision of the model's action potentials varies continuously along the spectrum, depends very little on the number of synaptic inputs, and is shown to be tightly correlated with the mean slope of the membrane potential preceding the output spikes. These results are shown to be largely independent of the size of postsynaptic potentials, of random background synaptic activity, and of shape of the correlated multisynaptic input pattern. An experimental test involving membrane potential slope is suggested to help determine the basic operating mode of an observed cortical neuron.

As the technology for simultaneously recording from many brain locations becomes more available, more and more laboratories are measuring the cross-correlation between single-neuron spike trains, and between composite spike trains derived from several undiscriminated cells recorded on a single electrode (multiunit clusters). The relationship between single-unit correlations and multiunit cluster correlations has not yet been fully explored. We calculated the normalized cross-correlation (NCC) between single unit spike trains and between small clusters of units recorded in the rat somatosensory cortex. The NCC between small clusters of units was larger than the NCC between single units. To understand this result, we investigated the scaling of the NCC with the number of units in a cluster. Multiunit cross-correlation can be a more sensitive detector of neuronal relationship than single-unit cross-correlation. However, changes in multiunit cross-correlation are difficult to interpret uniquely because they depend on the number of cells recorded on each electrode and because they can arise from changes in the correlation between cells recorded on a single electrode or from changes in the correlation between cells recorded on two electrodes.