  We formulate a novel technique for the detection of functional clusters in
discrete event data. The advantage of this algorithm is that no prior knowledge
of the number of functional groups is needed, as our procedure progressively
combines data traces and derives the optimal clustering cutoff in a simple and
intuitive manner through the use of surrogate data sets. In order to
demonstrate the power of this algorithm to detect changes in network dynamics
and connectivity, we apply it to both simulated neural spike train data and
real neural data obtained from the mouse hippocampus during exploration and
slow-wave sleep. Using the simulated data, we show that our algorithm performs
better than existing methods. In the experimental data, we observe
state-dependent clustering patterns consistent with known neurophysiological
processes involved in memory consolidation.
