  Background: Current neuronal monitoring techniques, such as calcium imaging
and multi-electrode arrays, enable recordings of spiking activity from hundreds
of neurons simultaneously. Of primary importance in systems neuroscience is the
identification of cell assemblies: groups of neurons that cooperate in some
form within the recorded population.
  New Method: We introduce a simple, integrated framework for the detection of
cell-assemblies from spiking data without a priori assumptions about the size
or number of groups present. We define a biophysically-inspired measure to
extract a directed functional connectivity matrix between both excitatory and
inhibitory neurons based on their spiking history. The resulting network
representation is analyzed using the Markov Stability framework, a graph
theoretical method for community detection across scales, to reveal groups of
neurons that are significantly related in the recorded time-series at different
levels of granularity.
  Results and comparison with existing methods: Using synthetic spike-trains,
including simulated data from leaky-integrate-and-fire networks, our method is
able to identify important patterns in the data such as hierarchical structure
that are missed by other standard methods. We further apply the method to
experimental data from retinal ganglion cells of mouse and salamander, in which
we identify cell-groups that correspond to known functional types, and to
hippocampal recordings from rats exploring a linear track, where we detect
place cells with high fidelity.
  Conclusions: We present a versatile method to detect neural assemblies in
spiking data applicable across a spectrum of relevant scales that contributes
to understanding spatio-temporal information gathered from systems neuroscience
experiments.
