Hierarchical Coupled Geometry Analysis for Neuronal Structure and Activity Pattern Discovery

TL;DR

This paper introduces a hierarchical coupled geometry analysis method that uncovers hidden connectivity and dynamic activity patterns in neuronal data across multiple time scales, enhancing understanding of neural network complexity.

Contribution

The proposed approach uniquely combines hierarchical data structures and multiscale metrics to analyze neuronal activity and connectivity in a data-driven manner.

Findings

Effective extraction of neuronal activity patterns.

Identification of temporal trends linked to behaviors.

Revealed hidden connectivity structures in neural data.

Abstract

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.