Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Hierarchical Coupled Geometry Analysis for Neuronal Structure and
  Activity Pattern Discovery   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.

19
The categories of this paper are: eess
21
The categories of this paper are: adap-org
17
The categories of this paper are: q-fin
4
The categories of this paper are: cond-mat
13
The categories of this paper are: quant-ph
1
The categories of this paper are: cs
31
The categories of this paper are: ao-sci
29
The categories of this paper are: funct-an
14
The categories of this paper are: math-ph
0
The categories of this paper are: hep-ph
8
The categories of this paper are: hep-ex
20
The categories of this paper are: acc-phys
9
The categories of this paper are: nlin
22
The categories of this paper are: chao-dyn
37
The categories of this paper are: supr-con
30
The categories of this paper are: q-alg