Identification of neurons participating in cell assemblies

Chances to detect assembly activity are expected to increase if the spiking activities of large numbers of neurons are recorded simultaneously. Although such massively parallel recordings are now becoming available, methods able to analyze such data for spike correlation are still rare, because it is often infeasible to extend methods developed for smaller data sets due to a combinatorial explosion. By evaluating pattern complexity distributions the existence of correlated groups can be detected, but their member neurons cannot be identified. In this contribution, we present approaches to actually identify the individual neurons involved in assemblies. Our results may complement other methods and also provide the opportunity for a reduction of data sets to the ldquorelevantrdquo neurons, thus allowing us to carry out a refined analysis of the detailed correlation structure due to reduced computation time.