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 talk, we will describe 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 data-driven organization of neurons and dynamic patterns in 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 technique to in-vivo neuronal recordings demonstrates the capability of extracting neuronal activity patterns and identifying temporal trends, associated with
particular behavioral events and manipulations introduced in the experiments.
Joint work with Gal Mishne, Ron Meir, Jackie Schiller, Uri Dubin and Ronald R. Coifman.