  Identifying the spatio-temporal network structure of brain activity from
multi-neuronal data streams is one of the biggest challenges in neuroscience.
Repeating patterns of precisely timed activity across a group of neurons is
potentially indicative of a microcircuit in the underlying neural tissue.
Frequent episode discovery, a temporal data mining framework, has recently been
shown to be a computationally efficient method of counting the occurrences of
such patterns. In this paper, we propose a framework to determine when the
counts are statistically significant by modeling the counting process. Our
model allows direct estimation of the strengths of functional connections
between neurons with improved resolution over previously published methods. It
can also be used to rank the patterns discovered in a network of neurons
according to their strengths and begin to reconstruct the graph structure of
the network that produced the spike data. We validate our methods on simulated
data and present analysis of patterns discovered in data from cultures of
cortical neurons.
