  In the wake of recent advances in experimental methods in neuroscience, the
ability to record in-vivo neuronal activity from awake animals has become
feasible. The availability of such rich and detailed physiological measurements
calls for the development of advanced data analysis tools, as commonly used
techniques do not suffice to capture the spatio-temporal network complexity. In
this paper, we propose a new hierarchical coupled geometry analysis, which
exploits the hidden connectivity structures between neurons and the dynamic
patterns at multiple time-scales. Our approach gives rise to the joint
organization of neurons and dynamic patterns in data-driven hierarchical data
structures. These structures provide local to global data representations, from
local partitioning of the data in flexible trees through a new multiscale
metric to a global manifold embedding. The application of our techniques to
in-vivo neuronal recordings demonstrate the capability of extracting neuronal
activity patterns and identifying temporal trends, associated with particular
behavioral events and manipulations introduced in the experiments.
