Hierarchical Coupled-Geometry Analysis for Neuronal Structure and Activity Pattern Discovery

Gal Mishne, Ronen Talmon, Ron Meir, Jackie Schiller, Maria Lavzin, Uri Dubin, Ronald R. Coifman

Research output: Contribution to journalArticlepeer-review


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 that implicitly takes into account the 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.

Original languageEnglish
Article number7548324
Pages (from-to)1238-1253
Number of pages16
JournalIEEE Journal on Selected Topics in Signal Processing
Issue number7
StatePublished - Oct 2016


  • Dimensionality reduction
  • diffusion maps
  • geometric analysis
  • manifold learning
  • neuronal data analysis

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Electrical and Electronic Engineering


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