Literature DB >> 17677845

Revealing network connectivity from response dynamics.

Marc Timme1.   

Abstract

We present a method to infer the complete connectivity of a network from its stable response dynamics. As a paradigmatic example, we consider networks of coupled phase oscillators and explicitly study their long-term stationary response to temporally constant driving. For a given driving condition, measuring the phase differences and the collective frequency reveals information about how the units are interconnected. Sufficiently many repetitions for different driving conditions yield the entire network connectivity (the absence or presence of each connection) from measuring the response dynamics only. For sparsely connected networks, we obtain good predictions of the actual connectivity even for formally underdetermined problems.

Year:  2007        PMID: 17677845     DOI: 10.1103/PhysRevLett.98.224101

Source DB:  PubMed          Journal:  Phys Rev Lett        ISSN: 0031-9007            Impact factor:   9.161


  36 in total

1.  Causal networks in simulated neural systems.

Authors:  Anil K Seth
Journal:  Cogn Neurodyn       Date:  2007-10-20       Impact factor: 5.082

2.  Correlations in spiking neuronal networks with distance dependent connections.

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Journal:  J Comput Neurosci       Date:  2009-07-01       Impact factor: 1.621

3.  GABA-mediated repulsive coupling between circadian clock neurons in the SCN encodes seasonal time.

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4.  A practical method for estimating coupling functions in complex dynamical systems.

Authors:  Isao T Tokuda; Zoran Levnajic; Kazuyoshi Ishimura
Journal:  Philos Trans A Math Phys Eng Sci       Date:  2019-10-28       Impact factor: 4.226

5.  State observation and sensor selection for nonlinear networks.

Authors:  Aleksandar Haber; Ferenc Molnar; Adilson E Motter
Journal:  IEEE Trans Control Netw Syst       Date:  2017-07-17

6.  Inferring the physical connectivity of complex networks from their functional dynamics.

Authors:  Hung Xuan Ta; Chang No Yoon; Liisa Holm; Seung Kee Han
Journal:  BMC Syst Biol       Date:  2010-05-26

7.  Scalability of Asynchronous Networks Is Limited by One-to-One Mapping between Effective Connectivity and Correlations.

Authors:  Sacha Jennifer van Albada; Moritz Helias; Markus Diesmann
Journal:  PLoS Comput Biol       Date:  2015-09-01       Impact factor: 4.475

8.  Inferring synaptic connectivity from spatio-temporal spike patterns.

Authors:  Frank Van Bussel; Birgit Kriener; Marc Timme
Journal:  Front Comput Neurosci       Date:  2011-02-01       Impact factor: 2.380

9.  Inferring network connectivity by delayed feedback control.

Authors:  Dongchuan Yu; Ulrich Parlitz
Journal:  PLoS One       Date:  2011-09-28       Impact factor: 3.240

10.  Synaptic scaling in combination with many generic plasticity mechanisms stabilizes circuit connectivity.

Authors:  Christian Tetzlaff; Christoph Kolodziejski; Marc Timme; Florentin Wörgötter
Journal:  Front Comput Neurosci       Date:  2011-11-10       Impact factor: 2.380

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