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  Inferring network connectivity from event timing patterns

Casadiego, J., Dimitra, M., & Timme, M. (2018). Inferring network connectivity from event timing patterns. Physical Review Letters, 121(5): 054101. doi:10.1103/PhysRevLett.121.054101.

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Genre: Journal Article

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 Creators:
Casadiego, Jose1, Author              
Dimitra, Maoutsa1, Author              
Timme, Marc1, Author              
Affiliations:
1Max Planck Research Group Network Dynamics, Max Planck Institute for Dynamics and Self-Organization, Max Planck Society, ou_2063295              

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Free keywords: Collective behavior in networks, Collective dynamics, Network structure, Neural information, Neuronal dynamics, Neuronal networks, Patterns in complex systems, Synapses, Topological tools
 Abstract: Reconstructing network connectivity from the collective dynamics of a system typically requires access to its complete continuous-time evolution, although these are often experimentally inaccessible. Here we propose a theory for revealing physical connectivity of networked systems only from the event time series their intrinsic collective dynamics generate. Representing the patterns of event timings in an event space spanned by interevent and cross-event intervals, we reveal which other units directly influence the interevent times of any given unit. For illustration, we linearize an event-space mapping constructed from the spiking patterns in model neural circuits to reveal the presence or absence of synapses between any pair of neurons, as well as whether the coupling acts in an inhibiting or activating (excitatory) manner. The proposed model-independent reconstruction theory is scalable to larger networks and may thus play an important role in the reconstruction of networks from biology to social science and engineering.

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Language(s): eng - English
 Dates: 2018-08-022018-08-03
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1103/PhysRevLett.121.054101
 Degree: -

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Title: Physical Review Letters
Source Genre: Journal
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Publ. Info: -
Pages: 6 Volume / Issue: 121 (5) Sequence Number: 054101 Start / End Page: - Identifier: -