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Inferring Network Connectivity from Event Timing Patterns.

Jose Casadiego1,2, Dimitra Maoutsa2,3, Marc Timme1,2,4,5

  • 1Chair for Network Dynamics, Institute of Theoretical Physics and Center for Advancing Electronics Dresden (cfaed), Technical University of Dresden, 01062 Dresden, Germany.

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Summary
This summary is machine-generated.

This study introduces a new theory to map network connections using only event timing data, overcoming limitations of continuous-time measurements. It reveals direct influences between system units, applicable from neural circuits to social networks.

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Area of Science:

  • Complex Systems
  • Network Science
  • Computational Neuroscience

Background:

  • Reconstructing network connectivity often requires continuous-time data, which is experimentally challenging.
  • Existing methods struggle with systems where only event time series are accessible.

Purpose of the Study:

  • To develop a theory for inferring physical connectivity from event time series.
  • To enable network reconstruction when continuous-time data is unavailable.

Main Methods:

  • Representing event timing patterns in an event space using interevent and cross-event intervals.
  • Linearizing an event-space mapping derived from model neural circuit spiking patterns.

Main Results:

  • Identified direct influences between units based on event timing patterns.
  • Successfully revealed synaptic connections (presence/absence) and coupling type (inhibitory/excitatory) in model neural networks.

Conclusions:

  • The proposed theory allows network reconstruction solely from event time series.
  • This model-independent approach is scalable and broadly applicable across scientific domains.